Title: Causality Meets the Table: Debiasing LLMs for Faithful TableQA via Front-Door Intervention
Venue: NeurIPS 2025
Accepted: yes
Keywords: TableQA

Causal

Large Language Model

Question Answering
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-16"}

comments: 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.

STRENGTHS AND WEAKNESSES:
- Strengths:
	- The application of causal inference methods to table question answering is novel.
	- The effectiveness of the proposed method is supported by extensive experiments.
	- The writing is clear and easy to follow.

- Weaknesses:
	- 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.
	- The use of LLaMA-2 in experiments is somewhat outdated.

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.
- 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.
- There appears to be a notation error in Equation (11) in Appendix B — specifically, a missing closing parenthesis.

LIMITATIONS:
Yes.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-26

comments: 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.

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.


Weaknesses see questions.

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.


For 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”).


For 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
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-07-02

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\n### 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.\n\nQUESTIONS:\nPlease refer to the Weaknesses part.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-04"}