Causality Meets the Table: Debiasing LLMs for Faithful TableQA via Front-Door Intervention
Authors: Zhen Yang
Ziwei Du
Minghan Zhang
Wei Du
Jie Chen
Fulan Qian
Shu Zhao
Venue: NeurIPS 2025 poster ()
Keywords: TableQA
Causal
Large Language Model
Question Answering
Primary area: deep_learning
TL;DR: We propose Causal Intervention TableQA (CIT), which is based on a structural causal graph and applies front-door adjustment to eliminate bias caused by token co-occurrence

Abstract: Table Question Answering (TableQA) combines natural language understanding and structured data reasoning, posing challenges in semantic interpretation and logical inference. Recent advances in Large Language Models (LLMs) have improved TableQA performance through Direct Prompting and Agent paradigms. However, these models often rely on spurious correlations, as they tend to overfit to token co-occurrence patterns in pretraining corpora, rather than perform genuine reasoning. To address this issue, we propose Causal Intervention TableQA (CIT), which is based on a structural causal graph and applies front-door adjustment to eliminate bias caused by token co-occurrence. CIT formalizes TableQA as a causal graph and identifies token co-occurrence patterns as confounders. By applying front-door adjustment, CIT guides question variant generation and reasoning to reduce confounding effects. Experiments on multiple benchmarks show that CIT achieves state-of-the-art performance, demonstrating its effectiveness in mitigating bias. Consistent gains across various LLMs further confirm its generalizability.

Causality Meets the Table: Debiasing LLMs for Faithful TableQA via Front-Door Intervention
Zhen Yangl.2, Ziwei D Minghan Zhang1.23 Wei Dul.23,
Jie Chen 12.3 Fulan Qian Shu Zhao School of Computer Science and Technology, Anhui University
Innformttion Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University
AAnhui Provincial Key Laboratory of Security Artificial Intelligence, Anhui University
zhaochuza2002hhotmaal..
Abstract
Table Question Answering (TableQA) combines natural language understanding and structured data reasoning, posing challenges semantic interpretation and logical inference. Recent advances in Large Language Models (LLMs) have improved TableQA performance through Direct Prompting and Agent paradigms. However, these models often rely spurious correlations, as they tend to overfit to token co-occurrence patterns pretraining corpora, rather than perform genuine reasoning. To address this issue, propose Causal Intervention TableQA (CIT), which is based on a structural causal graph and applies front-door adjustment to eliminate bias caused by token co-occurrence. CIT formalizes TableQA causal graph and identifies token co-occurrence patterns as confounders. By applying front-door adjustment, CIT guides question variant generation and reasoning reduce confounding effects. Experiments on multiple benchmarks show that CIT achieves state-of totiee-of-hhhann -tt-hha-tt performance, demonstrating its effectiveness in mitigating bias. Consistent gains across various LLMs further confirm its generalizability. We release our code here
Introduction
Tabular data is prevalent type of structured information, commonly found in many fields Yang et al.
2025
Lee et al. 2024 Xia et al. 2023]. Table Question Answering (TableQA), which aims to answer tables, plays key role in decision support and data analysis. Early
natural language questions
methods focused on SQL-based semantic parsing (Zhong et al.) [017] or pretraining on table-specific corpora (Ou and Liu 2022 Eisenschlos al.) |020 Xie et al. 2022]. More recently, Large Language
Models (LLMs) have achieved strong results on TableQA by leveraging In-Context Learning (Sui
al. 2023 Chen 2023a| and Chain-of-Thouge
(CoT) prompting [Cheng al. 2023 Ye al.
2023b
Based on CoT, two main paradigms have emerged: Direct Prompting (DP), which pertorms
natural language reasoning, and Agent, which relies on symbolic code execution.
Despite strong empirical performance, prompting strategies LLM-based TableQA are often not robust Ye et al. 2023a]. L.MMs tend to rely token co-occurrence patterns from pretraining data,
which can lead to spurious correlations and unfaithful reasoning [Lyu et al. 2023 Wang et al. 2023c
Bao et al. 2024 Turpin et al.) 2023 For example, shown in Figure phrases like exactly frequently co-occur with answers like yes, causing L.LMs to prefer yes even when no is correct.
"Corresponding authors
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Figure 1: Illustration of token co-occurrence bias as a confounder in LLM-based TableQA (a) Causal graph showing confounding reasoning; (b) Real-world example where pretraining bias causes unfaithful answers; (c) Do-intervention to block the back-door path; (d) Adversarial example demonstrating spurious correlations.
This creates confounder that affects both question interpretation and answer prediction, forming back-door path that distorts reasoning. Although causal methods have been proposed to address such issues [Niu al. 2021 Tian al. 2022 Guo et al. 2023). they often rely observable confounders or model internals, limiting their applicability in TableQA where confounders are typically latent.
Beyond the above qualitative analysis, also conduct quantitative verification of these limitations. Specifically, we apply double negation perturbations to logically equivalent examples from TabFact dataset [Chen et al. [2200] and observe substantial drop in accuracy. This suggests that LLMs rely more on surface-level linguistic patterns than on deep reasoning.
To address these challenges, we reinterpret TableQA from a causal perspective. As shown in Figure [a), ideal reasoning follows Q - F A, but token co-occurrence introduces confounder C. forming spurious path Q < C + A. We adopt front-door adjustment [Pearl et al. [016]. which enables causal estimation using only observed variables, without requiring access to or model internals. We propose Causal Intervention TableQA (CIT), a framework that mitigates bias from token co-occurrence patterns through front-door adjustment. CIT avoids explicit do-calculus by decomposing the adjustment process into four components: (1) Question Variant Generation, which produces diverse paraphrases of Q to reduce lexical bias; (2) Evidence Aggregation, which combines retrieved content across variants to improve coverage; (3) Answer Inference, which applies both DP and Agent reasoning strategies; (4) Joint Voting, which selects the final answer via majority voting. Experiments across multiple TableQA benchmarks and LLMs show that CIT consistently improves reasoning robustness and generalization. Our main contributions are as follows:
We are the first to introduce causal intervention into LLM-based TableQA by modeling token co-occurrence as latent confounding and applying front-door adjustment to mitigate its effect.
Causal formulation of co-occurrence bias:
We estimate causal effects using semantically diverse question variants. A single-pas pass generation strategy ensures low overhead.
Efficient intervention via question variants:
Broad empirical validation: CIT achieves state-of-the-as performance on multiple datasets across both open- and closed-source LLMs, demonstrating strong generalization.
2 Related Work
2.1 LLM-based TableQA
Recent advances large language models (LLMs) have greatly improved TableQA performance
by leveraging general reasoning capabilities (Pal et al. 2023 Lee et al. 2024 Zhong et al.) 2017
Yang al., 2025). Existing methods mainly follow two paradigms: Direct Prompting, which guides reasoning in a single step (Sui et al. 2023 Chen 2023a and Agent, which decomposes
the task into symbolic operations [Li et al. 2024b Lei et al. al.] 2023). Representative methods are
listed in Appendix A However, these approaches focus primarily guiding LLM reasoning, while

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overlooking a key issue: LLMs often encode token co-oceurrence patterns from pretraining data, which can induce spurious correlations between the question and the answer.
2.2 Causal Intervention
Causal inference offers principled framework for addressing bias through interventions (Pearl et al.
2016 Pearl 2019 Ren et al. 2023a b). Prior work has applied counterfactual [Niu et al. 202] 1
Xu al. 2023 Yang et 2023b back-door adjustment [Tian al.
2022 Zhuu al. [023] and
front-door adjustment Yang et al. 2021 Zhang et al. 2024a Yang et al. 2023a to mitigate spurious
correlations. Recent studies have extended ideas LLMs [Jin al. [023 Lyu et al. [0224]. although
many rely on heuristics or simplified causal graphs [Wang et al. 2023b Tang et al. 2023]. J. In contrast
back-door methods that require explicit modeling of confounders, which often infeasible for
LLMs, front-door adjustment enables causal estimation using only observed variables. This makes it particularly suitable for LLM--bsed TableQA.
Preliminaries
3.1 TableQA
Given question Q and table T, an LLM first interprets the question, retrieves relevant evidence E " T based on Q, and then reasons over Q and to produce the final answer A. This process can be formally expressed as Equation !
E Promptretrieee (Q,T), A = Promplanswer (Q,
Here, Promplratriew refers the step where the LLM selects evidence based on the question. Promptanswer performs reasoning over the question and the evidence to get the answer.
3.2 Structural Causal Model (SCM)
Causal inference offers a framework for modeling interventions and estimating causal effects. A central tool is the Structural Causal Model (SCM) et al.) 2016]. which represents dependencies among variables as a directed acyclic graph (DAG) G (V, E). where V is the set of nodes and E is the set of edges. In Figure [ we illustrate the causal graph constructed for the TableQA task and explain its components as follows.
Q Q E A. In TableQA, the input question Q determines the selection of evidence E, which in turn leads to the answer A. This forms the ideal causal path Q - E - A.
Q A. During pretraining, LLMs tend to overfit to token co-occurrence patterns in the corpus. In certain cases, this behavior interferes with reasoning and leads to biased predictions. We treat this as a latent confounder, denoted as C, which introduces a spurious back-door path Q C A.
do((). To identify the true causal effect, it is necessary to block the influence of the confounder C using the do-operator [Fenton et al. 2020]. an SCM, applying do(Q) corresponds to removing all incoming edges to Q, thereby el eliminating the indirect effect of C' on A through Q. In causal inference, the do-operator represents an ideal intervention that forcibly sets the value of a variable. However, such interventions are typically infeasible in observational data. Therefore, techniques such as front-door or back-door adjustment are commonly used to estimate causal effects without directly applying the do-operator.
3.3 Front-door Adjustment
Back-door adjustment requires access to the confounder C, which is unobservable in LLMs. In contrast, front-door adjustment bypasses this need and thus more applicable. By the law of total probability, the interventional distribution is given in Equation 2

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Figure 2: Overview of the CIT. Given question and table, CIT derives the final answer through: (1) Question Variant Generation, which produces semantically diverse variants; (2) Evidence Aggregation, which extracts and unifies evidence across variants; (3) Answer Inference, which combines Direct Prompting and Agent; and (4) Joint Voting, which selects the final answer via majority voting.
P(A do(()) = Σ P(A = do(Q = =)) =e do(Q =q)
(2)
By applying the law total probability and the assumptions of SCM, this expression can be further transformed into the final formula shown in Equation 3] The full derivation is provided in Appendix B
P(A do(Q 9)) = Σ P e Q= = Σ P(A= =e) =/) (3)
4 Method
By applying the do-operator, we block the bias introduced by the confounder C. Through front-door adjustment, this intervention can be reformulated into a tractable expression using only observed variables. Based on Equation 3 our method consists of four components: (1) Question Variant Generation, (2) Evidence Aggregation, (3) Answer Inference, and (4) Joint Voting. We describe each component in detail below.
4.1 P(Q q'): Question Variant Generation
To estimate P(Q d'), we generate of semantically equivalent question variants {g()% that preserve the intent the original question Q while differing in surface form. To reduce the cost of LLM inference, adopt single-past magle-pass generation strategy using prompt-based generator, denoted as prompt which produces all variants in single call, as shown in Equation Here, Q is the input question, T is the table, and the prompt used for variant generation is detailed in Appendix C
( LLM((, Prompl
Since all variants are generated simultaneously, their generation probability is approximated as uniform. As a result, P(Q = q') is treated as a constant and omitted from the final formulation.
Beyond the causal perspective, our method can also be understood semantically. When encoded by an LLM, original question and its variants are represented as high-dimensional vectors in a shared semantic space. Generating multiple variants effectively samples points around the original question, forming dense semantic neighborhood. This encourages the L.M to reason over meaning rather than surface form, which improves robustness and helps mitigate bias.

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4.2 Σ. P(E
9): Evidence Aggregation
To estimate E. P(E Q == q), we extract supporting evidence for the given question. Since CIT considers multiple question variants { evidence must be extracted for each variant. To reduce the cost, we adopt a separate single-pass strategy using Promptretrieve for evidence extraction. All question variants are provided to the LLM in one prompt, and the L.M extracts the corresponding evidence eyg for each variant and then aggregate form the final evidence, as shown in Equation The prompt used by Promptretriese is detailed in Appendix C
LLM(T, q.d', Promprrerieee)) c
e= -=Ue 1m1
e
eg
(5)
This union operation ensures that all potentially useful evidence across diverse paraphrases of the question is captured. By aggregating information from multiple linguistic perspectives, we construct more complete and robust foundation for downstream reasoning.
4.3
Ey = E= =
e, = PQQ d)): Answer Inference
To estimate Ege P(A=al e. P(Q q'), we infer under each variant q'. We consider two reasoning paradigms commonly used in LLM-based TableQA: Direct Prompting and Agent.
Direct Prompting (DP) Reasoning. DP guides the LLM to generate step-by-step reasoning via CoT to obtain the answer, improving performance on complex TableQA tasks. For each question variant q' the LLM generates token sequence conditioned o' and the retrieved evidence e, modeled autoregressively as Equation
P(A=a DP
E == ==)))=))-
II P(w wct,e,
(6)
Here, uy is the I-th token, Wel the preceding tokens, and the sequence length. The LLM maximizes this joint probability to generate reasoning steps and the final answer aP,, However, this formulation may overfit to frequent patterns from pretraining, leading to overconfident but unfaithful predictions. Our causal framework mitigates this by aggregating outputs across diverse question variants.
Symbolic Reasoning with Agent. In contrast to natural language reasoning, Agent allows the LLM to generate executable Python code for structured operations over table evidence. Given a question variant and evidence the LLM produces code snippet code(q', e), and the final answer is obtained by executing it within a Python shell as Equation 7 Here ezccute denotes run the code.
P(A= a Agent E e, Q = -') = ezecute(codeegg ,e)
This symbolic approach allows direct operations on tabular as filtering, aggregation, and arith- metic-enabling precise numerical. To reduce API cost, we adopt unified execution strategy: the original question and its variants set(g(} are processed in a single LLM call. The model reasons over each variant independently and aggregates intermediate results into a final answer Agent This one-shot process improves efficiency and ensures semantic consistency across variants.
4.4
Joint Voting
CIT supports both DP and Agent reasoning, and integrates their outputs via majority voting to
exploit their complementary strengths. For each question g. the framework performs n rounds of
DP and m rounds of Agent reasoning, yielding answer sets DP {a ..... a DP) and Agent
Agent {a
Om
Agent
The final prediction selected by majority vote over both sets as Equation 8
P(A=a a E e, == Majority Vote(A
Agent)
(8)
case of a tie, one of the top answers is selected uniformly at random. The hyperparameters control the number of DP and Agent rounds. We vary (n, m) in ablations to explore performance-cost
5

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trade-offs. Beyond voting, this joint design enhances semantic diversity by reasoning over multiple variants o' and evidence sets E. This is especially beneficial for ambiguous or biased questions. where aggregating diverse reasoning paths helps mitigate confounder-induced eroos.
5 Experimental Setup
5.1 Datasets and Evaluation
Dataset. We evaluate on three datasets: WikiTableQuestions (WTQ) Pasupat and Liang 2015). TabFact[Chen et al. 2020]. and FetaQA (Nan et al. 2022 WTQ involves aggregation, comparison, and arithmetic reasoning, with 4,344 test examples. TabFact is a fact verification task over 2,024 samples. FetaQA features free-form questions that require integrating information 2,003 samples.
Evaluation. Following prior work [Liu et al. 2024a Yang et al., 2025], we use exact match accuracy for WTQ and TabFact, which focus on short-form answers. For FetaQA, which requires long-form generation, we report BLEU [Papineni et al.) 2002 evaluate answer quality.
5.2 Implementation Details
To ensure fair comparison, first evaluate CIT using GPT-3.5 LLM. To assess generalizability, further test CIT across LLMs: Open-source: LLaMA 2-7B/13B/70B, DeepSeek-R1; Closed-source: GPT-3.5, GPT-4, GLM 4, Gemini 1.5, Claude 3.5, All LLMs temperature is 0.8.
Table Results on WikiTableQuestions with GPT-3.5. CIT-DP and CIT-Agent show re- sults for DP and Agent modes separately. CIT- AP&Agent shows the joint voting result.
Table 2: Results on TabFact dataset with GPT- 3.5. CIT-DP and CIT-Agent show results for DP and Agent modes separately. CIT- AP&Agent shows the joint voting result.
Method
Acc.
Method
Acc.
Omni'Tab (22' NAACL)
61.30
TAPAS-large (20' EMNLP)
81.00
Codex SQL (23' ICLR)
61.10
TAPEX-large (21' ICLR)
84.20
BINDER (23' ICLR)
64.60
SaMOE (22' ACL)
86.70
DATER (23' SIGIR)
65.90
SASP (22' ACL)
77.00
DTE (23' ACL)
54.20
T5-3B (22' EMNLP)
83.68
TACR (23' arXiv)
60.20
Codex end-to-end (23' ICLR)
72.60
ITR (23' ACL)
63.40
Codex SQL (23' ICLR)
80.70
StructGPT (23' EMNLP)
57.00
BINDER (23' ICLR)
85.10
Liu et al. (24' arXiv)
55.80
DATER (23' SIGIR)
85.60
Cabinet (24' ICLR)
69.10
StructGPT (23' EMNLP)
87.30
CHAIN-OF-TABLE (24' ICLR)
59.94
CHAIN-OF-TABLL (24' ICLR)
80.20
ReAcTable (24' VLDB)
68.00
ReAcTaable (24' VLDB)
86.10
SYNTQA (24' EMNLP)
70.40
Tab-PoT (24' arXiv)
85.77
Mix-SC (DP&Agent) (24' NAACL)73.65
Mix-SC (DP&Agent) (24' NAACL)
88.50
TIDE (DP& ADP&Agent) (25' ICLR)
75.00
TIDE (DP&Agent) (25' ICLR)
89.82
CIT-DP
65.40
CIT-DP
83.15
CIT-Agent
73.76
CIT-Agent
90.61
CIT-DP&Agent
76.38
CIT-DP&Agent
91.30
5.3 Baselines
We compare CIT with pretraining models and L.LM-base -bsse methods, include SASP
[Ou and Liu
2022
TAPAS-
Eisenschlos et al. 2020]. T5-3B [Xie et al. [022]. TAPEX- large
Liu et al.
202
Task Configs
Chen et al. 2023]. TARGET et al. 2024 TabCot [Chen] 2023b
TAG-QA Zhao
et al.
2023a
UniTabPT Sarkar and Lausen 2023 and Codex (Cheng et al.
2023
BINDER [Cheng
al.
2023
DATER Yee al. 20236), StructGPT [Jiang et 2022) DTE Waag et al.
2023a
TACR
[u al. 2023
ITR Lin et al.
2023 Tab-PoT [iao et al.
2024
[iu et al.
2024a

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CHAIN-OF -A-LLE Wang et al. 2024]. ReAcTable (Zhang et al. 2024c Cabinet Patnaik et al.
2024]. SYNTQA (Zhang 2024b), [Liu al. 2024b and TIDE Yang al. 2025 Details of
the baseline implementations are provided in Appendix
Results and Analysis
6.1 Main Results
Table [] shows that CIT achieves state-of-the- performance on WikiTableQuestions improving the previous best by 2.73%. On TabFact (T. it outperforms the strongest baseline by 2.21 %,, a relative gain of 21.71% % over the error rate. CIT also leads clearly on FetaQA (Table 3}. We analyze the effectiveness of CIT from both qualitative and quantitative perspectives: Qualitative Perspective: CIT introduces causal reasoning via a structural causal model, enabling identification and blocking of confounding bias through front-door adjustment. Question variants help mitigate spurious correlations from pretraining, improving answer faithfulness. Quantitative Perspective: On adversarial data with double negation (Figure T. CIT significantly outperforms non-intervention models, confirming its robustness. The combination of DP and Agent leverages complementary strengths and reduces reliance on any single reasoning mode.
Table 3: Results on FetaQA with GPT-3.5.
Table Impact of answer selection in CIT-DP and CIT-Agent.
Methods
BLEU( %
Task Configs (23' ACL)
27.80
Agent
DP
WTQ
TabFact
FetaQA
T5-large (23' SIGIR)
30.54
TAG-QA (23' ACL)
31.84
1
61.60
88.34
35.68
UniTabPT (23' NeurIPS)
33.12
3
66.11
90.42
35.93
Codex (23' SIGIR)
27.96
5
5
66.92
90.81
37.15
DATER (23' SIGIR)
30.92
3
64.46
85,42
35.98
TabCot (23' EACL)
29.36
1
61.14
89.18
35.73
TARGET (24' NeurIPS)
24.13
5
69.38
82.16
36.17
ReAcTTable (24' VLDB)
30.43
1
71.04
87.35
36.03
CIT-DP
36.15
5
66.62
86.51
35.99
CIT-Agent CIT-DP&Agent
33.65
3
76.38
91.30
36.34
36.34
We also observe that CIT-Agent consistently outperforms CIT-DP on WTQ and TabFact, likely due to its structured execution over tables and ability to handle large inputs with precise symbolic operations. On FetaQA, however, CIT-DP performs comparably, as BLEU evaluation favors the fluency of natural language outputs generated by DP, which better align with reference answers.
6.2 Effect of and in Answer Aggregation
To reduce bias from single-pas or single-mode inference. CIT performs n rounds Direct Prompting and rounds of Agent reasoning, aggregating all Table performance improves with larger n or m. More Agent results tend to yield better accuracy, while DP offers complementary signals. These results underscore the value of reasoning diversity and validate the design of our causal aggregation framework.
results via majority voting. As shown in
6.3 Ablation Study
To assess the contribution of each component, we conduct ablation studies under both DP and Agent. We consider two variants: (1) w/o Evidence Aggregation, which uses evidence extracted from the original question for all variants; and (2) w/o Question Variants, which disables variant generation and reasons directly over g. Results
3 that removing evidence aggregation yields minor drop, most variants retrieve similar evidence. In contrast, removing question variants leads significant decline, confirming their importance mitigating confounding bias. These findings validate the role of front-door variant generation in enabling robust, causally grounded inference.

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Table 5: Ablation results on datasets.
Method
WTQ
TabFact
FetaQA
CIT-DP
66.40
83.15
33.15
w/o Question Variants
62.66 (L 3.74)
76.38 (4 6.77) 79.64 (4 3.51)
31.71 (4 1.44)
w/o Evidence Aggregation
63.31 (4 3.09)
32.43 (4 0.72)
CTT-Agent
73.76
90.61
36.15
w/o Question Variants
68.39 (L 5.37)
86.86 (4 3.75)
34.21 (1 1.94)
w/o Evidence Aggregation
71.39 (4 2.37)
87.99 (4 2.62)
35.38 (4 0.77)
CIT-DP&Agent
76.38
91.30
36.34
w/o Question Variants
71.50 (L 4.88)
89.87 (4 1.43)
35.41 (1 0.93)
w/o Evidence Aggregation
74.47 (4 1.91)
90.27 (1 1.03)
36.02 (1 0.32)
6.4 Generalization Across LLMs
We evaluate CIT on diverse of open-source and closed-source LLMs, using the same setup as Section 5.2. As shown in Table [6] CIT consistently improves performance across all models. regardless of size, architecture, or pretraining corpus. These results highlight the transferability of our causal intervention framework and suggest that core TableQA challenges seallenges-semanti ambiguity, spurious correlations, and evidence selection- are shared across L.Ms..
Table 6: Comparison of LLMs with and without CIT.
Models
Init Accuracy ())
CIT Accuracy %))
Llama 2-7b
48.34
49.95 (1.61 f)
Llama 2-13b
50.18
52.07 (1.89 f)
Open- soures
Llama 2-70b
59.02
61.33 (2.31 f)
DeepSeek RI
78.38
80.48 (2.10 f)
GLM GPT
65.84
66.53 (0.69 f)
70.89
77.09 (6.20 f)
Closed-source
Gemini 1.5
61.56
66.51 (4.95 f)
Claude 3.5
72.33
75.87 (3.54 f)
6.5
Analysis of Influential Factors
Effect of Variant Quantity. As shown in Figure 3 accuracy improves with more question variants due to greater semantic coverage, but saturates beyond three. Token usage also increases sharply, especially during reasoning. We set the default to three variants to balance performance and efficiency.
de
Appd
wharg
whch
everage
Figure 3: Changes in accuracy and token consumption under different numbers of question variants.
Figure 4: Impact of question type CIT performance.

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Performance Across Question Types We analyze CIT across question types on WTQ and TabFact (Figure D. excluding FetaQA due BLEU's incompatibility with discrete categories. Questions are grouped by keywords (e.g., who, when, average). On TabFact, both modes perform well across types. WTQ. performance drops on numerical reasoning, especially aggregation. CTT-Agent further struggles due to hallucinated code constraints (e.g., unnecessary unique O). Despite this, the two modes show complementary strengths, supporting our joint reasoning.
Impact of LLM Size. We evaluate CIT using LLaMA 2 models of different sizes. As shown in 6 larger LLMs consistently yield better performance, likely due to their enhanced knowledge representation and reasoning capabilities. These strengths improve both the quality of question variants and the accuracy of evidence selection, which are critical to the effectiveness of front-door adjustment. This suggests that CIT benefits from scaling and can serve lightweight debiasing layer for increasingly powerful LLMs.
WTO
TabTact
FalaDA
12 93.90 09.23 10.00
19.70
75.42
07.01 36.16
erOdget
06.00
04.44
20:00
36.13
20
(1012) (2232))4411
11211) Table Soa
Table Site
Tale
Figure 5: Impact of table size TableQA performance.
Table Size Sensitivity. We evaluate CIT's robustness under varying table sizes by grouping test samples into bins of roughly 430 examples and computing average accuracy per bin (Figure While accuracy generally decreases with larger tables, CIT remains stable overall. CIT-Agent outperforms CIT-DP on larger tables, as it executes Python code over full tables, bypassing context length limits, whereas DP's reliance on in-context reasoning leads to degraded performance with long inputs.
Efficiency and API Usage. CIT is efficient and compatible with both open-source and closed- source LLMs. As shown in T performs well on non-API models like LLaMA and DeepSeek, supporting private deployment. For API-based use, CIT requires only three calls: one each for variant generation, evidence integration, and answer inference. Table 7 shows that CIT achieves strong performance with substantially fewer API calls than prior LLM-based methods.
Table 7: Comparison of methods with results and API calls
Methods CHAIN-OF-TABLE
Result
Number of API calls (Next Operation 1 Argument + Transform 1) * Iter N 3N
59.94
CIT
76.38 Generate Questions 1 + Evidence Integration 1 Answer =
Error Case Analysis. We manually examine 100 examples identify common sources of failure. For CIT-DP, most emrors arise from incorrect answer formatting and the inability to recognize special table lines such headers, footnotes, merged cells. For CIT-Agent, errors often involve halluci- nated constraints-sucl as adding non-exing non-existent conditions-an occasional format inconsistencies. These findings highlight the challenges of aligning LLM output with task-specific answer expectations. A detailed breakdown of error types and representative examples is provided in Appendix P
Additional Analyses. We further investigate whether CIT is affected by LLM data contamination, details are reported in Appendix F We also enumerate the range of question types that CIT can handle drreetively-incluuee where, when, which, what, who, is/does, how many, average, sum, wic,-with corresponding case examples shown in Appendix G

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7 Limitations
While CIT offers principled way to mitigate confounding bias, its effectiveness depends on the quality of question variants. Limited diversity or semantic inconsistency may hinder coverage of the original intent. Future work may explore controlled generation filtering improve variant quality.
Conclusion
We present CIT, causal intervention framework for TableQA that applies front-door adjustment to mitigate latent confounding bias LLM- reasoning. By modeling TableQA as structural causal process, CIT identifies and blocks spurious back-door paths introduced by pretraining. The method implements this via question variant generation, evidence aggregation, and joint reasoning with both Direct Prompting and Agent paradigms. Extensive results across multiple benchmarks and LLMSs demonstrate CIT's robustness, effectivvness, and generality.
Acknowledgements
Our work is supported by the National Natural Science Foundation of China (62476003), Anhui Province Excellent Scientific Research and Innovation Team (2024AH010004), Anhui Provincial Natural Science Foundation Water Science Joint Fund (2408055US006), the University Syn- ergy Innovation Program of Anhui Province (GXXT-2023-050, and SMP-Zhipu.Al Large Model Cross- Dioss-Disciplinary Fund 1SMP-Zhipu202402100,,, We also acknowledge the support from Zhipu AI-Anhui University Joint Research Center, and the High-Performance Computing Platform of Anhui University.
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19

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21

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A Related Work: LLM-based TableQA
Direct Prompting in TableQA. In Direct Prompting (DP), LLMs perform step-by-step reason- ing through serialized natural language prompts, often under the Chain-of-Thouuhe (CoT) frame- work KKng al. 2024 Zhao al., [023b] Deng al. 2024]. Early DP methods (Zhao al.
2023a Sui el al. 2023 Chemmengath et al. 2021] used few-shot examples, SQL-style prompls, or
zero-shot CoT help LLMS decompose and solve complex queries. For instance, Luo al. [Lu
et al. 2023 constructed CoT exemplars with retrieval-basee reconstruction, while BINDER (Cheng
al. 2023 composed sub-queries via SQL logic. DATER (Ye et al. [023b] guided reasoning
through SQL-based parsing and completion. More recently, (Liu et al. [024aa] explored zero-shot prompting with "think step by step" instructions to encourage implicit decomposition.
Agent in TableQA. In the Agent paradigm, LLMs analyze the question, plan steps, and generate Python code operate over tables (Li al. [0244]] Gong al. 2020). CHAIN-OF-TABLE [Wang et al. 2024] decomposes questions by creating intermediate tables and applying custom functions. ReAcTable [Zhang al.] [2024c] iteratively generates intermediate results and adapts subsequent
actions based on output. Other works [Liu et al. 2024b integrate SQL or Python- based agents for structured code-Ievel reasoning.
Joint DP and Agent. DP and Agent can be combined for joint reasoning. Mix-SC Liu et al. 2024a merges both paradigms and majority voting for answer selection. TIDE (Yang al.
2025] further introduces structured triplets to enhance decomposition. However, current methods focus on guiding reasoning without addressing token co-occurrence bias from LLM pretraining, which can introduce spurious correlations. To address this, we are the first to define LLM-bbasd TableQA from causal perspective and identify latent confounding in the reasoning process.
B Front-door Adjustment
Back-door adjustment requires explicit access to the confounding variable C. However, in our setting, the bias induced by LLM pretraining is latent and unobservable, rendering back-door adjustment inapplicable. Fortunately, the front-door adjustment criterion (Pearl et al. 2016] enables causal estimation without requiring access confounder values. It operates by intervening the treatment variable using the do-operator and leveraging an observed mediator that satisfies the front-door conditions.
do(Q)
E
(a)
(b)
Figure 6: The causal graph of TableQA.
Following the law of total probability, we derive the decomposition expressed in Equation
P(A=a do 9)) Σ P(A= do(Q =)) do(Q 9))
c)
(9)
By the back-door criterion, the intervention on E' does not alter the conditional distribution of A given Q and E. Hence, introducing the do-operator on E' does not affect the overall expression, yielding Equation 10
P(A = do(Q = =q)) = Σ P(A=aa do(Q = do e)) do(Q = -)) (10)
22

---

Under the structural causal model (SCM), since Q and E are connected via direct causal link without confounders, the do-operator on Q can be omitted, yielding Equation
P(A a do(Q -q) Σ P(A = do(Q q). do =10
(11)
In the SCM, Q and A not directly connected, intervening on does not affect the distribution of A, yielding Equation 12
P(A=a do q)) Σ = d(
=
(12)
Using the law of total probability, we can derive Equation 13
P(A do(Q ΣΣ P(A=o) /o
do(E ( I
c)) =
Using the same logic as in the transition from Equation to Equation 10
we proceed Equation
P(A a do(Q = = D E -= 10 =
(14)
Using the same logic as in the transition from Equation [0 Equation
we proceed Equation
P(A=a = do =0)
=
20=00/0000 = (15)
Finally, by reorganizing the summation terms, we obtain the Equation I6
P(A -a 1 = 4)) = Σ P(E=ele =)) > P(A=a - (16)
C Prompt
We provide the prompts for the three core components this section.
D Baselines
SASP Ou and Liu 2022 uses lexical and structural features generate programs for solving pseudo programs. TAPAS- large [Eisenschlos et al.) 2020 creates balanced dataset of millions of automatically generated training examples for intermediate learning before fine-tunte fine-tuning. T5-3B Xie et al. 2022] within the Unified SKG framework unifies 21 SKG tasks into text-to-text format comprehensive SKG research. TAPEX-large [Liu et al., [02T] learns a neural SQL executor on synthetic corpus of executable SQL. queries and their outputs. Task Configs [Chen et al. 2023 structured compositional task prompts improve multi-task learning and zero-shot generalization for table-to-text models. TARGET Ji et al. 2024 is a benchmark for table retrieval in generative tasks, evaluating retriever performance and downstream impacts on QA, fact-checking, and text- to-SQL. TabCot [Chen 2023b LLMs excel at table reasoning via chain-of-
prompting, matching specialized models without table-specific training. TAG-QA (Zhao et al. 2023a] pioneers graph-guided + knowledge-oe ledge--eeeeeteed TableQA for long-form answers. UniTablTT [arrar and
Lausen 2023 Unified table- luble-pretrained LLMs (T5-based) that outperform specialized models across parsing/ QA/classification at scale (770M-11B).
Codex [Cheng et al. 2023], as an OpenAl API, can generate SQI. or Python statements and perform
end-to-end QA. BINDER CCengg et al. 2023 combines end-to-end and symbolic approaches,
23

---

Instruction: Bescd on the table content, goncrate qucstion similar to the original question without changing its main content, and write it after generate questions:'.
Example:
Table:
/"
table caption stay in office
name took office left office party
William McCreary March 4, 1803 March 1809 Democratic Republican
Alexander McKim March 1809 March 3, 1815 Democratic Republican
William Pinkney March 1815 April 18, 1816 Democratic Republican |
Peter Little September 2, 1816 March
Democratic Republican
Peter Little March 4, 1623 March 1825 Jacksonian DR
Peter Little March 4, 1825 March 3, 1829 Adams
Benjamin C. Howard March 4, 1829 March 3, 1833 Jacksonian
Question: How many people stayed at least years in office?
generate questions:
1. The total number of people stay at least years in office?
2. How many peaple stayed more than 3 years office?
3. How many people served in office for 3 years or more?
Test:
Table:
1"
table caption: : (TITLE)
(TABLE)
Figure 7: The prompt of question variants generation.
Analyze the initial questions the generated similar questions based on the table, lind the relevant evidence related to each question, and summarize the questions to obtain the total evidence, and write after' evidence:'.
Instruction:
Test:
Table:
/*
table caption (TITLE)
(TABLC)
Question: (QUESTION)
Figure 8: The prompt of evidence aggregation.
generating and iteratively refining pseudo-SQL queries construct final answers. For TableQA. DATER YYe al. 2023b) extracts relevant sub-tables and decomposes questions to reason jointly over them. StructGPT [Jian et al. 2023 enhances zero-shot reasoning by iterating through specialized interfaces for structured data. DTE Wang et al.] [023a] generates counterfactual examples to refine text-to-SQL question answering. TACR [Wu et | [023]] aligns multi-hop questions with different
modalities for accurate evidence retrieval. ITR [Lin al. 2023] selects relevant rows and columns to
form a compact sub-table for efficient reasoning.
Liu et al. 2024b creates new tables with external information, enabling queries over both original and new tables to answer. CHAIN-OF OF-TABLE (Wang et al.) 2024] dynamically plans operation chains based on table structure and associated questions. ReAc Table [Zhang et al.) 2024c] uses LLMs to iteratively generate intermediate tables, with external code execution for accuracy. Cabinet [Patnaik et al. (2024) removes irrelevant noise in tables to improve LLM reasoning accuracy.
Mix-SC [L et al. [0.24a explores the combination of CoT and PyAgent to address L.M sensitivity
to table structure. SYNTQA Zhang et al. 2024b] unifies Text-to-SQL (arithmetic/lon tables) and

---

Instruction: the generate questions similar questians that arise from the question and help reasoning the original question. Reasoning gives the answer to each generate question step by step, then reasoning the final answer by referring to generate questions and question. is only "Final Answer: form, no answer number entity names, short possible, without any explanation.
enswer
ensure
Example:
Table:
/"
iconstituency number name reserved for (sc/st/none) district number of etectorates (2009)
tikamgarh none likamgarh 153,339
44 Jatara tikamgarh 145,555
45| prithvipur none tikamgarh 138,110|
46 niwari none tikamgarh 141,265
khargapur none tikamgarh
48 maharsjpur none| chhatarpur 162,460
151 chhatarpur none chhatarpur 152,505
52| bijawer none chhatarpur 151,159
total total total 1,207,039
*
Qucstion: Which district has the groatest total numbar of electoratos?
generate questions:
Which district has highest
number electorates?
Which district has the highost electorate count?
3. Which district has the la rgest number of electorates? Answer:
count the electorate Tikamgarh has highest total number electorates. Tikamgarh has the highest electorate count. 3. Tikemgarh District ....., so answcr is Tikamgarh.
the number...
answers abave, Tikamgarh greatest Final Answer: Tikamgarh
electorates.
Test:
Table:
/"
table caption : (TITLE)
(TABLE)
Question: (QUESTION)
generate questions: (GENCRATE)
Answer:
Figure 9: The prompt of answer inference.
E2E TQA (ambiguitylschemas) via answer selection, boosting performance, TIDE (Yang et al. 2025 structuring triples to help LLMs decompose and validation reasoning context.
E Data Contamination
Mitigating Data Contamination with CIT. Data contamination is common concern in L.M--based methods, where test samples may appear in the model's training data. To evaluate the robustness of CIT, we compare the performance of direct answering with that of CIT-based reasoning. As shown in Table CIT achieves approximately 27% higher accuracy, indicating that its effectiveness primarily stems from the method itself rather than potential data leakage.
Table 8: Comparison of direct QA for data contamination.
Models
Accuracy( %)
Direct QA CCeng et al. 2023
48.70
CIT
76.38
25

---

Description
1939/40
194D/41
1941/42
1942/43
1943/44
1944/45
Total
Direct War Losses
360,000
NaN
NaN
NaN
NaN
183,000
543,000
Murdered
75,000
100,000
116,000
133,000
82,000
NaN
506,000
Deaths Prisons
69,000
210,000
220,000
266,000
381,000
NaN
1,148,000
& Camps
Deaths Outsido Prisons Camps
3
NaN
42,000
71,000
142,000
218,000
NaN
473,000
Murdered In Eastern Regions
4
NaN
NeN
NaN
NaN
NaN
100,000
100,000
Doaths in Other
Countries
NaN
N&N
NaN
NaN
NaN
NaN
2,000
6
Total
504,000
352,000
407,000
541,000
681,000
270,000
2,770,000
Question:
how many people murdered in 19/0/417
Gold Answer:
100,000
Reason Answer:
100000
Error Analysis:
Incorrect answer format
Question:
what is the last description of losses this chart?
Gold Answer:
Deaths other countries
Reason Answer:
Total
Error Analysis:
Unable to recognize special line (total)
Figure 10: Incorrect answer format and unable to recognize special line errors.
Date
Competition
Location
Country
Event
Plecing
Nationellty
31 October 2008
2008-09 World Cup
Manchester
United Kingdam
Keirin
2
GBR
31 October 2008
2008-09 World Dup
Manchester
United Kingdom
Sprint
G3R
November 2005
2008-09 World Cup
Manchester
United Kingdom
500 m time trial
G3R
November 2008
2008-08 World Cup
Manchester
United Kingdom
Sprint
G3R
November
Cup
Manchester
Kingdom
sprint
November 2005
International
Manchestar
United Kingdom
Intarnational
2 November 2006
2008-09 World Cup
Manchester
United Kingdom
Team sprint
G3R
2 November 2008
2008 09 World Cup
Monchester
United Kingdom
Keirin
G3R
2 November 2005
2008-09 World Cup
Manchester
United Kingdom
Team sprint
93R
February 2009
2008-09 World Cup
Copenhagen
Denmerk
Team sprint
G3R
February 2009
2008-09 World Cup
Copenhagen
Denmark
Team sprint
G3R
Fobruary
2008-09 World Cup
Coacnhegon
February 2009
2008-09 World Cup
Copenhegen
Denmark
Sprint
G3R
Kingdom
October
Kingdom
Sprint
30 October 2009
2009-10 World Cup
Manchester
United Kingdom
Keirin
G3R
October 2009
2009-10 World
Manchester
United Kingdom
trial
G8R
November 2009
2009-10 World Cup
Manchester
United Kingdom
Team sprint
G3R
November 2009
2009-10 World Cup
Marcchester
United Kingdom
Tear sprint
G3R
November 2009
2009 10 World Cup
Manchester
United Kingdom
Team sprint
G3R
Question:
what is the total number of competition?
Gold Answer
Reason Answer:
crnque_competitions off[Compattioor]luniqqeee) Final answer
Error Analysis:
Hallucination adds conditions (unique)
Figure 11: Halluciantion adds extra conditions error.
26

---

Rank
Cyclist
Team
Time
UCI ProTour Points
Alajandro Valverde (ESP)
Caisse Epargne
5h
40
Alaxandr Kolobnev (RUS)
Taam CSC Saxo Ba nk
8.1.
30
Davide Rebellin (ITA)
Gerolsteiner
s.t.
25
Paolo Bettini (ITA)
Quick Step
s.t.
20
Franco Pellizotti (ITA)
Liquigas
s.t.
15
Denis Menchav (RUS)
Rabobenk
s.t.
11
7
Semuel Sánchez (ESP)
Euskaltel- Euskedi
s.t.
Stěphane Goubert (FRA)
Ag2n-La Mondiale
+2'
Halmar Zubeldia (ESP)
Euskaltel-Euskedl
10
David Moncoutié (FRA)
Oofidis
+2'
Question:
who was ranked next after davide rebellin?
Gold Answer:
Paolo Bettini (ITA)
Reason Answer:
Paolo Bettini
Error Analysis:
Incorrect answer format
Figure 12: Incorrect answer format error.
Error Case
G Question Types
Question:
what country had the cyclists?
Question
1. which country had the highest number of cyclists?
Variant:
2. which nation had the largest representation of cyclists?
3. which country were most cyclists from?
Question:
who was ranked between denís menchov and stephane goubert?
Question Variant:
which cyclist was positioned between denis menchov and stéphane goubert?
2. who ranked just ahead of stéphane goubert and just behind denis menchov? who the cyclist that came in between denis menchov and stéphane goubert the rankings?
Question:
which year has the most deaths outside of prisons & camps?
Question Variant:
1. which year were there highest number of deaths outside prisons camps?
2. which year had the greatest number of deaths outside of prisons & camps?
3. in which year were the deaths outside of prisons & camps the highest?
Question:
where was the last competition played?
Question
what the location of final competition?
Variant:
in which venue did the last competition take place?
3. where did hannes hopley compete last?
Figure 13: Type of wh- questions.
27

---

Question:
how long did take the industrial quest to complete the course?
Question
1. what was the elapsed time for the industrial quest to finish the race?
Variant:
2. how long the industrial quest take complete sydney to hobart yacht race?
3. what was the total time taken by the industrial quest finish the course?
Question:
how many total films have they already appeared in?
Qucstion
1. what is the total number of films they have appeared in?
Variant:
2. how many films radhika pandit featured
3. what is the count of films that radhika pandit has acted in?
Question:
in cycle of austria's next top model, what the average of all the contestants' ages?
Question
what average age
contestants in cycle austria's next topmodel?
Variant:
2. what is the mean age of the contestants cycle of austria's next topmodel?
3. what is the average age of participants the fourth cycle of austria's 's next topmodal?
Figure 14: Type of how- and average questions.
28


Reviews:
[{"review_id": "4TerMlZpOZ", "reviewer": "Reviewer_MAtw", "summary": "The paper identifies a key failure mode in TableQA with LLMs: reliance on spurious correlations arising from token co-occurrence in pretraining data. It introduces Causal Intervention TableQA (CIT), a framework that uses front-door adjustment to counteract latent confounding. CIT operates via: Question variant generation, Evidence aggregation, Dual-mode reasoning (Direct Prompting and Agent-based symbolic code execution), and Joint voting across outputs. Experiments on WikiTableQuestions, TabFact, and FetaQA show state-of-the-art results. The framework is tested across a variety of LLMs (open and closed source), showing robustness and generalization.", "questions": "Please refer to the Weaknesses part.", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "### Strengths\n\n- The paper convincingly argues that LLMs often give unfaithful answers due to lexical biases and pretraining artifacts. The failure cases are real and well-demonstrated.\n\n- Applying front-door adjustment in the TableQA context is novel and theoretically justified. The causal graph modeling (Q ← C → A) is intuitive.\n\n- CIT outperforms competitive baselines across multiple datasets and models. The performance boost is nontrivial—especially the 6.2% absolute gain with GPT-4 and nearly 2% improvement over top baselines like TIDE.\n\n- The paper includes ablations, breakdowns by question type, table size, and model scale, and evaluates robustness to adversarial perturbations. These analyses add significant value.\n\n- The single-pass strategies for variant generation and reasoning reduce token/API cost while maintaining strong performance.\n\n- Demonstrating gains on both closed and open-source models enhances credibility and practical relevance.\n\n### Weaknesses\n\n- The front-door adjustment is implemented in a heuristic way (i.e., generate variants, do majority vote), not through formal estimation of interventional distributions.\n\n- The expression in Equation 3 is not estimated via probabilistic modeling; rather, it's approximated through aggregation/voting. Thus, the causal framing may feel more metaphorical than operational.\n\n- Most components (variant generation, majority voting, agent reasoning) are reused from existing paradigms. The innovation lies in framing and orchestration rather than in novel modeling or algorithms. (I'm not saying that it cannot be innovative by putting together existing techniques. )\n\n- Mix-SC and TIDE already explore DP + Agent combinations. The gains of CIT could partly stem from better prompt engineering or more voting, rather than causal reasoning.\n\n- There is an inherent tension between latent confounders (which are unobserved) and front-door adjustability (which assumes a valid mediator). The assumptions behind the SCM and validity of the front-door criterion are not fully scrutinized.\n\n- There is no proof that CIT truly eliminates the confounding bias—it’s mostly argued by performance gains.\n\n- Accuracy or BLEU is used as a proxy, which doesn't directly test causal reasoning improvement.\n\n- Some notational inconsistencies and redundant derivations across the main text and appendix could be tightened.", "was_revised": true, "final_justification": "The author responses have addressed parts of my concerns. Therefore, I raised the score a bit.", "rebuttal": "We thank the reviewer for the detailed critiques. Below, we respond to each concern in turn.\n\n### Q1: The front-door adjustment is implemented heuristically, without formal distributions.\n\n**(1) The black-box nature of closed-source LLMs makes probability estimation infeasible:** We agree that CIT approximates front-door adjustment through deterministic procedures rather than formal probabilistic modeling. This is a practical necessity, as the internal distributions of closed-source LLMs (only get token) are not observable or trainable. This limitation causes the use of structure-guided heuristics in place of full distributional estimation.\n\n**(2) CIT structurally follows the front-door criterion:** Although we do not explicitly compute interventional probabilities, our framework adheres to the structural decomposition of front-door adjustment:\n\n- Question variant generation approximates P(Q=q′);\n- Evidence aggregation estimates P(E∣Q=q);\n- Answer inference approximates P(A∣E=e,Q=q').\n\nThis corresponds directly to Eq. 3. Each component in CIT plays the role of a counterpart in the front-door adjustment, ensuring that the **causal structure is faithfully preserved**, even if the operations are deterministic.\n\n**(3) Prior work has used causal as an effective heuristic:** Studies [1-2] have adopted causal not for exact identifiability, but as **heuristic guides** for system design in settings where exact confounder distributions or interventions are unavailable. Our work follows this tradition: we use the causal graph and front-door to **guide module design**, enable interpretability, and mitigate lexical bias via structured reasoning. The resulting gains support the practical value of this heuristic.\n\n- [1] Yang Z, Liu Y, Ouyang C. Causal Intervention-based Few-Shot Named Entity Recognition[C]//The 2023 Conference on Empirical Methods in Natural Language Processing.\n- [2] Ren L, Liu Y, Ouyang C. Causal inference-based debiasing framework for knowledge graph completion[C]//International semantic web conference. Cham: Springer Nature Switzerland, 2023: 328-347.\n---\n\n### Q2: The method primarily reuses known components.\n\n**(1) Causal framing provides theoretical foundations that facilitate future method development:** Our work is the first to introduce causal intervention into LLM-based TableQA, framing token co-occurrence as a confounder and modeling the task using a structural causal graph. This formulation not only justifies our design of question variants and evidence mediation, but also **provides a principled foundation for future methods to build upon and make theoretically grounded improvements.**\n\n**(2) Existing components are leveraged to ensure effective and efficient implementation:** We use mature components to ensure that CIT inherits the best practices of existing paradigms, while being driven by a novel causal objective: mitigating bias induced by token-level confounding. As our results shown, this combination leads to **consistent performance gains**, validating the effectiveness of causal guidance even when applied through standard building blocks.\n\n---\n\n### Q3: Improvements may stem from better prompting or voting.\n\n**(1) Prompts are consistent with prior work:** We largely adopt the same prompting strategies as Mix-SC and TIDE for both DP, PyAgent and voting. Despite this, CIT achieves SOTA, suggesting that the gains stem from our **causal design rather than prompt tuning**.\n\n**(2) Adversarial results(Figure 1) show that CIT mitigates confounding bias:** When using identical components and prompts, models without CIT exhibit significant results degradation under double negation perturbations. In contrast, **CIT remains robust**, indicating that its effectiveness arises from explicitly addressing confounding via causal intervention.\n\n**(3) Removing causal components leads to performance drops:** Ablation results (Table 5) demonstrate that removing key components related to front-door —such as variant generation— leads to notable accuracy degradation. This confirms the **functional role of causal components in enhancing robustness.**\n\nOverall, the strong performance of CIT cannot be explained by prompt alone; rather, it results from **causally grounded mitigation of confounding bias** inherent in task.\n\n---\n\n### Q4: Tension between latent confounders and front-door assumptions; is the SCM valid?\n\n**(1) Our SCM is carefully constructed based on the TableQA task pipeline and LLM behavior.**\nThis causal graph aligns with how LLMs use evidence in the reasoning process [3–7], and it satisfies the structural requirements of SCMs. While we acknowledge that SCM is an abstraction, it is grounded in actual system behavior and has solid theoretical foundations.\n\n- [3] Ye Y, Hui B, Yang M, et al. Large language models are versatile decomposers: Decomposing evidence and questions for table-based reasoning[C]//Proceedings of the 46th international ACM SIGIR conference on research and development in information retrieval. 2023: 174-184.\n- [4] Wang Z, Zhang H, Li C L, et al. Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding[C]//The Twelfth International Conference on Learning Representations.\n- [5] Lin W, Blloshmi R, Byrne B, et al. An inner table retriever for robust table question answering[C]//Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023: 9909-9926.\n- [6] Wu J, Xu Y, Gao Y, et al. TACR: A Table-alignment-based Cell-selection and Reasoning Model for Hybrid Question-Answering[J]. arXiv preprint arXiv:2305.14682, 2023.\n- [7] Luo T, Lei F, Lei J, et al. Hrot: Hybrid prompt strategy and retrieval of thought for table-text hybrid question answering[J]. arXiv preprint arXiv:2309.12669, 2023.\n\n**(2) The front-door adjustment is applicable in our setting and is empirically supported.**\nIn the SCM framework, back-door adjustment requires the confounder C to be observable. In contrast, **front-door adjustment allows C to be unobserved by introducing a mediator E and intervening through the do-operator, which blocks the confounding path Q←C→A**. This ensures the validity of causal inference. The formal justification can be found in [8–9] and our Appendix B.\n\n- [8] Pearl J, Glymour M, Jewell N P. Causal inference in statistics: A primer[M]. John Wiley & Sons, 2016.\n- [9] Norman E. Fenton, Martin Neil, and Anthony C. Constantinou. The book of why: The new science of cause and effect, judea pearl, dana mackenzie. basic books (2018). *Artif. Intell.*, 284:103286, 2020\n\n**(3) Front-door adjustment has been shown effective in many studies for handling unobserved confounders and improving performance.**\nInspired by these findings, our method incorporates a mediating path to avoid dependence on unobservable variables and enables more robust causal estimation.\n\n- [10] Yang X, Zhang H, Qi G, et al. Causal attention for vision-language tasks[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021: 9847-9857.\n- [11] Yang Z, Liu Y, Ouyang C, et al. Improving few-shot named entity recognition with causal interventions[J]. Big Data Mining and Analytics, 2024.\n\n**(4) We also provide empirical evidence supporting the validity of our causal structure:**\nRemoving the evidence module or question variants leads to a significant drop in performance (Table 5);\nIn addition, adversarial examples (Figure 1) show that CIT effectively mitigates the confounding bias caused by token co-occurrence, whereas other methods fail under the same setting.\n\nIn summary, both the SCM construction and the application of front-door adjustment in our method are theoretically sound and empirically validated.\n\n---\n\n### Q5: No direct evidence that CIT eliminates confounding bias.\n\nWe acknowledge the challenge of measuring latent confounding. Nevertheless, we offer **three complementary forms of indirect evidence**:\n\n- **Adversarial robustness**: Double negation examples cause large drops for vanilla models, but CIT is more stable;\n- **Ablations**: Removing causal front-door components degrades results;\n- **Model transferability**: CIT improves across open-/closed-source LLMs (Table 6), suggesting bias reduction generalizes.\n\nThese collectively support the bias mitigation effect.\n\n---\n\n### Q6: Evaluation metrics are indirect; causal reasoning is not directly tested.\n\n(1) First, we adopt **Accuracy and BLEU as our primary metrics to ensure consistency and fair comparison with prior LLM-based TableQA work [12-13].**\n\n- [12] Liu T, Wang F, Chen M. Rethinking Tabular Data Understanding with Large Language Models[C]//Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024: 450-482.\n- [13] Yang Z, Du Z, Zhang M, et al. Triples as the Key: Structuring Makes Decomposition and Verification Easier in LLM-based TableQA[C]//The Thirteenth International Conference on Learning Representations. 2025.\n\n(2) While we acknowledge the **absence of ground-truth causal labels and the difficulty of directly measuring latent confounding**, we provide **three complementary forms of indirect evidence** supporting the bias mitigation effect:\n\n- **Adversarial robustness**: Double negation examples lead to substantial performance drops for baseline models, whereas CIT remains stable (Figure 1);\n- **Ablation studies**: Removing components related to front-door adjustment—such as question variants—significantly degrades performance (Table 5);\n- **Model transferability**: CIT yields consistent gains across both open- and closed-source LLMs (Table 6), suggesting that the reduced confounding bias generalizes across model families.\n\nTogether, these results offer strong indirect support for the causal benefits of CIT.\n\n---\n\nWe hope these responses address your concerns. We are grateful for your feedback and would welcome further discussion if needed."}, {"review_id": "tglWe1M62d", "reviewer": "Reviewer_SVNh", "summary": "The paper proposes Causal Intervention TableQA (CIT) which uses a structural causal graph and front-door adjustment to reduce confounding caused by token co-occurrence biases. It achieves state-of-the-art performance across benchmarks, with consistent gains across various regimes confirming its generalizability.", "questions": "A key issue is the SCM could be misspecified. For the SCM, how to verify there is no edge between C and E? For example, the confounding may impact the table (i.e., evidence). How to verify there is no edge directly from Q to A? Any causal conclusion could be invalidated without rigorously verifying the specification.\n\n\nFor Figure 1 (left), why is the error caused by token co-occurrence? It seems more like a mismatch of complete information in the question instead (because it overly stresses on “exactly 12” and misses the keyword “Cambridge University”).\n\n\nFor experiments, why is the temperature set to 0.8? Would higher/lower temperature have an impact on the causal intervention? As a next step, how to mitigate hallucinated constraints?", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "The paper is well-written in general. The method of front-door adjustment looks novel and effective to address the key issue of token-occurrence in LLM-based table QA. The decomposition is simple in nature, yet each step of execution is grounded. The experiments are extensive on different baselines.\n\n\nWeaknesses see questions.", "was_revised": true, "final_justification": "My concern is addressed and I maintain my score.", "rebuttal": "We thank the reviewer for the constructive feedback, encouraging recognition, and helpful questions. We are glad to clarify the raised points and respond in detail below.\n\n---\n\n### Q1: Potential misspecification of the SCM: how to justify no edge from C→E or direct Q→A link?\n\n**（1）On C→E**: We posit that the confounder C (token co-occurrence) influences evidence E only through its impact on Q. **In practice, evidence selection is conducted by prompting the LLM with Q;** hence, E is a downstream result of Q. There is no direct dependency between C and E in our pipeline, satisfying the front-door criterion.\n\n**（2）On Q→A**: The Q→E→A path is motivated by both empirical observation and established TableQA literature, and is further grounded in the Structural Causal Model (SCM), which underpins our front-door adjustment design. **Several prior works [1-5] formulate TableQA as below process: evidence retrieval conditioned on the question, followed by answer inference based on the retrieved content.** This supports our assumption that E fully mediates the influence of Q on A.\n\n- [1] Ye Y, Hui B, Yang M, et al. Large language models are versatile decomposers: Decomposing evidence and questions for table-based reasoning[C]//Proceedings of the 46th international ACM SIGIR conference on research and development in information retrieval. 2023: 174-184.\n- [2] Wang Z, Zhang H, Li C L, et al. Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding[C]//The Twelfth International Conference on Learning Representations.\n- [3] Lin W, Blloshmi R, Byrne B, et al. An inner table retriever for robust table question answering[C]//Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023: 9909-9926.\n- [4] Wu J, Xu Y, Gao Y, et al. TACR: A Table-alignment-based Cell-selection and Reasoning Model for Hybrid Question-Answering[J]. arXiv preprint arXiv:2305.14682, 2023.\n- [5] Luo T, Lei F, Lei J, et al. Hrot: Hybrid prompt strategy and retrieval of thought for table-text hybrid question answering[J]. arXiv preprint arXiv:2309.12669, 2023.\n\nAlthough some recent methods adopt an end-to-end format, they often rely implicitly on latent retrieval steps within the model’s reasoning process. In contrast, our method explicitly enforces reasoning through E, ensuring that answers cannot be derived directly from Q alone. **This not only satisfies the front-door criterion, but also encourages step-by-step reasoning rather than shortcut predictions.**\n\n**（3）Empirical support**: We observe that when front-door intervention is disabled (e.g., by removing question variants in Table 5), model predictions degrade significantly, **indicating the presence of confounding and validating our causal formulation.**\n\n---\n\n### Q2: Figure 1 seems more like misunderstanding of “Cambridge University” than token co-occurrence.\n\nTo ensure that the error shown in Figure 1 is not a coincidence tied to the presence of \"Cambridge University,\" we provide multiple forms of empirical evidence to validate the presence of co-occurrence bias:\n\n- **（1）Adversarial perturbation (Figure 1)**: We apply double negation to logically equivalent questions in TabFact and observe a sharp performance drop. This demonstrates that **token-level perturbations, even without altering semantic meaning, significantly affect the model's prediction—indicating reliance on surface co-occurrence patterns rather than reasoning.**\n- **（2）Ablation on question variants (Table 5)**: When we remove question variant generation while keeping the question semantics intact, model performance declines. **This confirms that variants help mitigate co-occurrence bias**, which aligns with the function of front-door adjustment in our causal framework.\n- **（3）Support from existing literature**: Prior study [6] have shown that **increasing input diversity improves reasoning robustness and reduces overfitting to spurious cues**. These findings indirectly support our hypothesis and intervention strategy.\n  - [6] Maini P, Seto S, Bai R, et al. Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling[C]//Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024: 14044-14072.\n\nTogether, these results confirm that the error in Figure 1 is not an isolated artifact, but rather a representative case of co-occurrence-induced confounding—one that our method is specifically designed to address.\n\n---\n\n### Q3: Why temperature=0.8? How does it affect intervention? How to mitigate hallucinated constraints?\n\n（1）**Follow prior studies** [7-8]: We chose temperature 0.8 based on prior studies, **as it provides a good trade-off between diversity and factual consistency**—both of which are essential for robust front-door estimation.\n\n- [7] Liu T, Wang F, Chen M. Rethinking Tabular Data Understanding with Large Language Models[C]//Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024: 450-482.\n- [8] Yang Z, Du Z, Zhang M, et al. Triples as the Key: Structuring Makes Decomposition and Verification Easier in LLM-based TableQA[C]//The Thirteenth International Conference on Learning Representations. 2025.\n\n（2）**More results with other temperatures**: We further tested temperatures 0.4 and 1.0. Lower values lead to overly deterministic answers (hurting variant diversity), while higher values increase hallucinations. **Our empirical results show that 0.8 achieves the best balance.**\n\n| temperatures | result on WTQ |\n| ------------ | ------------- |\n| 0.4          | 75.62         |\n| 0.8          | **76.38**     |\n| 1.0          | 75.95         |\n\n\n\n（3）Hallucination is a well-known and broadly acknowledged challenge in current LLMs. \n\n- Our work does not aim to fully eliminate hallucinations, but rather to **constrain them in a principled way via causal intervention**. Specifically, We incorporate **Agent-based symbolic execution** and **majority voting** across multiple reasoning paths, which serve to regularize the model’s outputs and reduce overconfident or spurious answers.\n\n- As a next step, our method provides a **modular causal framework that is compatible with other hallucination mitigation strategies**. For example, it can be integrated with retrieval-augmented generation, symbolic consistency checks, or post-hoc verification modules to further enhance LLM robustness in TableQA.\n\nWe sincerely thank you again for your valuable comments and insightful questions, which have helped us improve the clarity and rigor of our work. We hope our responses address your concerns, and we welcome further discussion if there are any remaining questions."}, {"review_id": "OvsWvcRs6C", "reviewer": "Reviewer_fTKY", "summary": "This paper applies causal inference methods to the table question answering task and proposes a front-door intervention approach to mitigate potential spurious correlations in table QA.", "questions": "- It is unclear why Q1, Q2, and Q3 in Figure 2 can be considered semantically equivalent question variants, as claimed in line 115. A clearer justification would be helpful.\n- In Sections 4.2 and 4.3, the authors handle the first and second terms of Equation (3) separately. However, both terms share the same evidence $e$, making it difficult to treat them independently. The authors should explain the rationale behind this separation.\n- There appears to be a notation error in Equation (11) in Appendix B — specifically, a missing closing parenthesis.", "limitations": "Yes.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "- Strengths:\n\t- The application of causal inference methods to table question answering is novel.\n\t- The effectiveness of the proposed method is supported by extensive experiments.\n\t- The writing is clear and easy to follow.\n\n- Weaknesses:\n\t- Cost concern: Although the authors claim that their single-pass generation reduces time and cost, the method still requires multiple API calls per question. Each call involves significantly more tokens than the original query due to the inclusion of multiple question variants. This raises concerns about the method’s practicality.\n\t- The use of LLaMA-2 in experiments is somewhat outdated.", "was_revised": true, "final_justification": "The author addressed all of my questions point by point and provided relevant experimental data as support. Based on this, I still maintain a supportive attitude toward this article.", "rebuttal": "We thank the reviewer for the constructive feedback and insightful comments. We are encouraged by your recognition of the novelty of applying causal inference to TableQA and the strength of our empirical results. Below, we respond to each concern in detail:\n\n------\n\n### Q1: Cost concern — multiple API calls and increased tokens due to variants.\n\n**（1）Local LLM support**: We support fully local deployment of LLMs, eliminating API latency and commercial cost. Notably, as shown in Table 6, CIT achieves **strong performance even on local open-source LLMs**, demonstrating its efficiency and portability.\n\n| Local LLM   | Init Acc | +CIT Acc |\n| ----------- | -------- | -------- |\n| Deepseek R1 | 78.38    | 80.48    |\n|  |  |  |\n\n**（2）Token usage comparison:** Beyond the API call count in Table 7, we also compare the token consumption of different methods. Results show that our approach **uses fewer tokens** than most multi-step methods (e.g., CHAIN-OF-TABLE, TIDE). Although it consumes slightly more tokens than Mix-SC, it achieves **substantially higher accuracy**, which we believe is a worthwhile trade-off, especially in **high-stakes applications where precision is critical**.\n\n| Method         | Token consumption         | Acc in WTQ |\n| -------------- | ------------------------- | ---------- |\n| CHAIN-OF-TABLE | （780+20+10）\\*N = 810\\*N | 59.94      |\n| Mix-SC         | 210+300 = 510             | 73.65      |\n| TIDE           | 1200+210+300+970=2680     | 75.00      |\n| Ours CIT       | 260+70+210+300+320=1160   | 76.38      |\n|  |  |  |\n\n**（3）Ablation justification**: As further evidenced in Table 5, removing question variants causes a significant drop in performance. This confirms that **the modest increase in token usage is not only acceptable but essential for mitigating confounding bias**.\n\n------\n\n### Q2: Use of LLaMA-2 — concern about outdated models.\n\n**（1）At the time of paper submission，LLaMA-2 remains a representative open-source baseline. Moreover, it provides multiple model sizes** (7B, 13B，70b), allowing us to investigate the **impact of model scale**. As shown in Table 6, performance consistently improves with model size, validating the scalability of our approach.\n\n**（2）Generalization to newer models:** CIT achieves consistent gains on recent models such as **DeepSeek R1 and Claude 3.5**, demonstrating its robust generalization to both open- and closed-source systems.\n\n**（3）Updated results with Qwen 3**: We have added experiments using the latest Qwen 3 series, and CIT continues to yield improvements, further confirming the **effectiveness and adaptability** of our method.\n\n| Model     | Init Acc | +CIT Acc     |\n| --------- | -------- | ------------ |\n| Qwen 3-8b | 74.27    | 76.52(2.25↑) |\n|  |  |  |\n\nTogether, these results underscore that CIT is **not tied to a specific model**, and its benefits persist across architectures and model generations.\n\n------\n\n### Q3: Justification of question variants (Q1, Q2, Q3 in Figure 2).\n\n**(1) Semantic consistency of Q1, Q2, and Q3**: These are **semantically equivalent variants** that differ only in surface phrasing. All three variants query the same underlying intent: whether **four teams scored 12 points against Cambridge University**.\n\n**(2) Quantitative validation of variant quality**: To ensure that the generated variants are indeed meaning-preserving, we compute **BERTScor and cosine similarity between sentence embeddings**. Results confirm high similarity, validating that the variants are lexically diverse yet semantically consistent.\n\n| BERTScore           | (Q1, Q2)=81.24, (Q1, Q3)=85.44, (Q2, Q3)=76.42       |\n| ------------------- | ---------------------------------------------------- |\n| Cosine Similarities | (Q1, Q2) = 95.10, (Q1, Q3) = 94.98, (Q2, Q3) = 89.90 |\n|  |  |  |\n\n**(3) Toward quality control in future work**: These metrics can also be adopted in future research as automatic filters to select or prune low-quality variants, further enhancing the robustness of CIT when deployed in practice.\n\n------\n\n### Q4: Why handle the two terms in Eq (3) separately, given that both involve the same evidence e?\n\nThank you for this insightful question. We understand the concern and would like to clarify our interpretation and implementation.\n\n**（1）A clearer operational interpretation：** While both terms in Equation (3) involve the symbol e, they correspond to **distinct roles within the front-door decomposition**:\n\n- P(E=e∣Q=q) reflects the **observational process** of retrieving evidence from the question.\n- P(A=a∣E=e,Q=q′) estimates the model’s behavior under variant phrasing q′, **approximating a simulated intervention**.\n\nAlthough both involve e, their conditional contexts differ. We find that **treating them separately provides a clearer operational interpretation** and avoids mixing observational retrieval with counterfactual reasoning.\n\n**（2）adopt a tractable approximation:** Moreover, we recognize that theoretically, E is a random variable. Therefore, as described in Equation (5), we adopt a tractable approximation: we retrieve evidence from multiple semantically equivalent variants and **take their union as a surrogate for marginalization over E**.\n\n**（3）Empirical support:** This merged evidence set  is computationally tractable and has shown empirically strong performance (Table 5). While it is indeed an approximation of full marginalization, we think it remains **faithful to the spirit of front-door adjustment**, and provides a **stable and interpretable basis for causal estimation** in practical LLM-based TableQA scenarios.\n\n------\n\nMeanwhile, thank you for pointing out the writing error—we will correct the notation issue in the revised version. We hope our responses have addressed your concerns. Thank you again for your thoughtful review. We would be glad to provide any further clarification or discussion if helpful."}, {"review_id": "KIFY69uHSf", "reviewer": "Reviewer_LdwC", "summary": "This paper introduces a framework designed to debias LLMs for Table Question Answering. The core problem addressed is that LLMs often rely on spurious correlations from pretraining data instead of genuine reasoning, leading to unfaithful answers.\n\nThe proposed framework formalizes TableQA within a causal framework, identifying token co-occurrence as a latent confounder. To mitigate this bias it applies front-door adjustment. The framework consists of four main components: 1. producing diverse paraphrases of the input question, 2. combining retrieved content across variants, 3. utilizing both Direct Prompting and 4. selecting the final answer from aggregated results. \n\nExperiments across multiple TableQA benchmarks and various LLMs demonstrate that this approach achieves state-of-the-art performance and shows consistent gains.", "questions": "1. Could the authors provide a more in-depth discussion on the limitations and assumptions of the front-door adjustment in the context of TableQA, particularly regarding the assumption that all causal paths from question (Q) to answer (A) are mediated by evidence (E)? \n\n2. Given that the quality and diversity of question variants are critical to CIT's effectiveness, could the authors explore more sophisticated methods for generating these variants beyond a single-pass prompt-based approach? \n\n3. The paper shows that models learn qualitative rankings but do not approximate the true underlying distribution. Could the authors elaborate on whether this qualitative understanding is truly sufficient for all aspects of causal reasoning in TableQA, especially for tasks requiring precise quantitative inference, or if it introduces inherent limitations for certain types of questions?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n* The paper addresses a well-identified problem in LLM-based TableQA: the reliance on spurious correlations from pretraining data.\n* The paper introduces a principled causal framework for understanding and mitigating bias in TableQA by modeling token co-occurrence as a latent confounder and employing front-door adjustment. \n* Empirical results are strong, demonstrating state-of-the-art performance on multiple TableQA benchmarks (WTQ, TabFact, FetaQA) using various LLMs.\n* The ablation studies demonstrate the contribution of key components, particularly the importance of question variant generation in mitigating confounding bias.\n\n\nWeaknesses:\n\n* The effectiveness of CIT relies heavily on the quality and semantic diversity of the generated question variants. Limited diversity or semantic inconsistency could hinder the method's ability to cover the original intent and mitigate bias effectively.\n* While the paper acknowledges this limitation and suggests future work on controlled generation or filtering, the current approach to generating question variants (single-pass, prompt-based) might still be susceptible to the LLM's own biases or limited creativity in paraphrasing.\n* The reliance on front-door adjustment, while suitable for latent confounders, depends on the strong assumption that \"all causal paths from Q to A are mediated by E\". While the paper depicts Q -> E -> A as the ideal path, and Q <- C -> A as the spurious one, real-world TableQA scenarios might have more complex causal structures where E does not strictly mediate all causal effects from Q to A.\n* The quantitative verification of limitations uses double negation perturbations, which is a specific type of adversarial example. While effective, it might not encompass the full range of spurious correlations or reasoning failures LLMs exhibit in TableQA.\n* While the paper demonstrates generalizability across various LLMs, the performance still varies across different models and question types, indicating that CIT is a debiasing layer rather than a complete solution for all LLM limitations in TableQA. For instance, numerical reasoning and aggregation remain challenging for some LLMs even with CIT.", "was_revised": true, "final_justification": "The authors have clarified many of the issues raised in the original review, I remain supportive of accepting this paper.", "rebuttal": "We sincerely thank the reviewer for the thoughtful and constructive review. Below, we address your concerns point by point.\n\n------\n\n### Q1: Validity of front-door assumptions\n\n**（1）Architectural Enforcement of Mediation:** Our framework is intentionally designed to enforce the mediating role of E via two core mechanisms:\n\n- **Explicit Retrieval Module**: The system must first extract relevant evidence EE from the table TT before any reasoning is performed, effectively preventing direct Q→A shortcuts.\n- **Isolated Reasoning Module**: Answer generation is strictly conditioned on EE, ensuring that all downstream inference depends on retrieved evidence.\n\n**（2）Alignment with Established TableQA Paradigms:** Our Q→E→A pipeline is consistent with widely adopted TableQA methodologies:\n\n- [1] Ye Y, Hui B, Yang M, et al. Large language models are versatile decomposers: Decomposing evidence and questions for table-based reasoning[C]//Proceedings of the 46th international ACM SIGIR conference on research and development in information retrieval. 2023: 174-184.\n- [2] Wang Z, Zhang H, Li C L, et al. Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding[C]//The Twelfth International Conference on Learning Representations.\n- [3] Lin W, Blloshmi R, Byrne B, et al. An inner table retriever for robust table question answering[C]//Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023: 9909-9926.\n\n**（3）Empirical support**: When front-door intervention is disabled (Table 5), results degrade significantly, **indicating the presence of confounding and validating our causal formulation.**\n\nTogether, these architectural constraints, empirical results, and alignment with existing best practices provide strong support for the mediator role of E in our causal framework.\n\n------\n\n### Q2: Simplicity of variant generation strategy\n\n**（1）CIT aims to balance diversity and semantic consistency:** As shown in Appendix C, we guide the LLM to generate diverse question variants by incorporating prompted exemplars and paraphrasing instructions. These variants are generated in a single-shot format, which helps ensure that all variants are grounded in the same semantic intent, thus reducing the risk of semantic drift.\n\n**（2）Data diversity remains a general challenge in LLM-based task, not specific to CIT:** As noted in [4], ensuring sufficient diversity while preserving label consistency is a widely acknowledged research challenge for task.\n\n- [4] Maini P, Seto S, Bai R, et al. Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling[C]//Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024: 14044-14072.\n\n**（3）Even with a small number of variants, CIT outperforms some baselines**. As shown in Figure 3, CIT achieves better performance than several comparison methods under a limited-variant setting, indicating that **our generation process yields high-quality, semantically meaningful variants**.\n\n**（4）The SCM-based formulation of CIT supports flexible extensions in future work**. Since CIT models TableQA as a causal process grounded in SCM, it naturally supports the integration of alternative variant generation strategies. For example, one could incorporate reasoning-style control, use similarity-based selection, or apply retrieval-augmented rephrasing techniques.\n\n------\n\n### Q3: Causal reasoning vs. numerical precision\n\n**（1）Numerical reasoning remains a general limitation of LLMs, not just in TableQA.** [5] has shown that tasks involving precise arithmetic or aggregation are still challenging for current models.\n\n- [5] Xu F, Lin Q, Han J, et al. Are large language models really good logical reasoners? a comprehensive evaluation and beyond[J]. IEEE Transactions on Knowledge and Data Engineering, 2025.\n\n**（2）CIT already achieves state-of-the-art performance** compared to strong baselines, including on numerical question types (see Tables 1–3 and Figure 6), suggesting it helps mitigate spurious correlations even in such settings.\n\n**（3）The PyAgent module in CIT offers partial support** by enabling symbolic execution for numerically grounded questions (Section 4.4), though some limitations remain (e.g., hallucination, grounding errors).\n\n**（4）CIT is modular by design**, and can flexibly incorporate additional components—such as symbolic reasoning tools, consistency checks, or numerical validators—to further strengthen quantitative capabilities.\n\n------\n\n### Q4: Is qualitative understanding sufficient for causal reasoning?\n\n**（1）Qualitative causal framing yields robust improvements across settings.** As shown in Tables 1–3 and 6, incorporating causal reasoning—via variant aggregation and evidence mediation—consistently improves performance across datasets and models. This suggests that our approach, while primarily qualitative in design, **effectively mitigates shortcut biases and improves robustness in diverse settings**.\n\n**（2）Many prior TableQA works similarly focus on qualitative ranking over explicit probability modeling.** Several recent methods [6-7] use direct prompting (DP), agent-based execution (e.g., PyAgent), and majority voting as their main prediction mechanisms. These approaches emphasize reasoning consistency and answer selection**, aligning with our use of causal semantics over full distribution estimation.\n\n- [6] Liu T, Wang F, Chen M. Rethinking Tabular Data Understanding with Large Language Models[C]//Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024: 450-482.\n- [7] Yang Z, Du Z, Zhang M, et al. Triples as the Key: Structuring Makes Decomposition and Verification Easier in LLM-based TableQA[C]//The Thirteenth International Conference on Learning Representations. 2025.\n\n**（3）That said, we acknowledge that probabilistic modeling remains valuable—especially for numerical and arithmetic questions.** CIT addresses this partially through the Python-based agent module, which allows symbolic execution for questions requiring precision. Nevertheless, our error analysis reveals that issues like numerical inconsistency or symbolic hallucination persist in certain cases. Future work could **enhance CIT by incorporating** logit-based probability estimation, numerical consistency checks, or hybrid symbolic-neural modules to better support fine-grained quantitative reasoning.\n\n------\n\n### Q5: Scope of adversarial verification — is double negation too limited?\n\n**（1）Our goal is not to cover the full space of spurious correlations, but to demonstrate that such bias exists and impacts performance.** The sharp performance drop under double negation perturbations (Figure 1) provides concrete evidence that **LLM-based TableQA systems are vulnerable to co-occurrence bias**. While this is only one type of perturbation, the observed degradation clearly confirms the presence of confounding effects, regardless of which specific scenario they arise from.\n\n**（2）Multiple evaluation angles further support CIT's effectiveness in mitigating this bias.** Across diverse datasets, LLMs types, and question formats, Tables 1–3, 6 and Figure 4 consistently show **CIT outperforms baselines, indicating it generalizes beyond any single adversarial setup and effectively addresses TableQA**.\n\n**（3）Our error analysis provides guidance for future exploration of additional perturbation types.** In Section 5.3, we present diverse failure cases across tasks and model behaviors. These qualitative insights help identify where CIT is effective and where it could be extended—offering **a roadmap for broader adversarial coverage in future work**.\n\n---\n\n### Q6: CIT is a debiasing layer rather than a complete solution for all LLM limitations in TableQA..\n\n**（1）Numerical and symbolic reasoning remain challenging across tasks and base LLMs—not just in TableQA.** As widely discussed in studies [5,8], even strong LLMs often struggle with precise arithmetic, aggregation, or multi-step symbolic consistency. These issues are known limitations of current model architectures and are orthogonal to any specific domain formulation.\n\n- [8] Deng S, Dong H, Si X. Enhancing and evaluating logical reasoning abilities of large language models[C]//ICLR 2024 Workshop on Secure and Trustworthy Large Language Models. 2024.\n\n**（2）Despite these challenges, CIT achieves better performance than baselines, including on numerical questions.** As shown in Figure 6 and Tables 1–3, **CIT consistently outperforms others, demonstrating that it effectively reduces spurious correlations even in numerically grounded scenarios.** While CIT may not solve all arithmetic limitations, it improves robustness by encouraging evidence-grounded inference.\n\n**（3）CIT is designed as a modular and extensible framework.** Its formulation not only guides causal interpretation but also allows **integration of complementary techniques** to enhance LLM. For example, future extensions could incorporate symbolic consistency checks, program verification, or external numerical toolkits, making CIT a flexible foundation for tackling broader reasoning limitations.\n\n- [9] Pan L, Albalak A, Wang X, et al. Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning[C]//Findings of the Association for Computational Linguistics: EMNLP 2023. 2023: 3806-3824.\n- [10] Patel N, Kulkarni M, Parmar M, et al. Multi-LogiEval: Towards Evaluating Multi-Step Logical Reasoning Ability of Large Language Models[C]//Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024: 20856-20879.\n\nWe again thank the reviewer for the careful reading and helpful feedback. We hope our responses address your concerns, and we would be happy to provide further clarification or discussion if needed."}]

Author rebuttal:

Meta-review: This paper introduces Causal Intervention TableQA (CIT), a framework that addresses a fundamental failure mode in LLM-based TableQA: reliance on spurious correlations arising from token co-occurrence in pretraining data. By framing TableQA within a structural causal model and applying a front-door adjustment strategy through question variant generation, evidence aggregation, dual-mode reasoning, and joint voting, the paper provides both a principled causal perspective and a practical solution.

The strengths of this work are clear. Reviewers unanimously highlight that the problem is well-motivated and convincingly demonstrated. The causal framing is novel in the TableQA context, and while many of the components draw on existing paradigms, the orchestration guided by causal principles leads to consistent and significant improvements. CIT achieves state-of-the-art performance across multiple benchmarks (WikiTableQuestions, TabFact, FetaQA) and models (both open- and closed-source), with strong ablations, adversarial robustness experiments, and analyses across question types and table sizes. Importantly, the author rebuttal clarified concerns about the causal assumptions, the heuristic implementation of front-door adjustment, and the role of question variants, and provided additional experimental evidence that CIT’s gains cannot be explained by prompt tuning or voting alone.

The weaknesses raised—such as the heuristic nature of front-door adjustment, reliance on variant generation quality, and remaining challenges in numerical reasoning—are valid but do not detract from the paper’s overall contribution. The authors’ rebuttal addressed these points thoroughly, situating their work within the tradition of using causal framing as a heuristic guide when full probability estimation is infeasible. Reviewers agreed that the responses strengthened the case for acceptance, with most maintaining or raising their scores.
Decision: Accept (poster)