Debiasing Multimodal Models via Causal Information Minimization
Authors: Vaidehi Patil
Adyasha Maharana
Mohit Bansal
Venue: EMNLP 2023 Findings (2023)
Keywords: Multimodal
Causality
Debiasing
Out-of-distribution

Abstract: Most existing debiasing methods for multimodal models, including causal intervention and inference methods, utilize approximate heuristics to represent the biases, such as shallow features from early stages of training or unimodal features for multimodal tasks like VQA, etc., which may not be accurate. In this paper, we study bias arising from confounders in a causal graph for multimodal data, and examine a novel approach that leverages causally-motivated information minimization to learn the confounder representations. Robust predictive features contain diverse information that helps a model generalize to out-of-distribution data. Hence, minimizing the information content of features obtained from a pretrained biased model helps learn the simplest predictive features that capture the underlying data distribution. We treat these features as confounder representations and use them via methods motivated by causal theory to remove bias from models. We find that the learned confounder representations indeed capture dataset biases and the proposed debiasing methods improve out-of-distribution (OOD) performance on multiple multimodal datasets without sacrificing in-distribution performance. Additionally, we introduce a novel metric to quantify the sufficiency of spurious features in models' predictions that further demonstrates the effectiveness of our proposed methods.

Debiasing Multimodal Models via Causal Information Minimization
Vaidehi Patil
Adyasha Maharana
Mohit Bansal
UNC Chapel Hill
(vaidehi, adyasha, mbansal yecs. udc.edu
Abstract
Counterfactual Image
Original Image
[cup removed)
Most existing debiasing methods for multi- modal models, including causal intervention and inference methods, utilize approximate heuristics to represent the biases, such as shal- low features from early stages of training or unimodal features for multimodal tasks like VQA, etc., which may not be accurate. In this paper, we study bias arising from confounders in a causal graph for multimodal data, and ex- amine a novel approach that leverages causally- motivated information minimization to learn the confounder representations. Robust predic- tive features contain diverse information that helps model generalize to data. Hence, minimizing the information con- of features obtained from pretrained bi- ased model helps learn the simplest predictive features that capture the underlying data distri- bution. We treat these features confounder representations and use them via methods mo- tivated by causal theory to remove bias from models. We find that the learned confounder representations indeed capture dataset biases and the proposed debiasing methods improve out-of-distributionn (OOD) performance on mul- tiple multimodal datasets without sacrificing in-distribution performance. Additionally, we introduce novel metric quantify the suffi- ciency spurious features in models' predic- tions that further demonstrates the effectiveness of our proposed methods.'
Question: What is the color toaster?
Model
Answer
Model
Answer
LXMERT
blue
X
LXMERT
blue
x
DVQAf
blue
DVQAF
red
V
Ours
red
Ours
red
Figure 1: Multimodal models tend to rely on spurious correlations the dataset answer a question. Existing methods remove unimodal biases whereas our method removes biases arising from cross cross-modal interactions well and is more invariant to irrelevant features (e.g., the coffee mug) in this example.
is asked (Wen et al., 2021) (see Fig. 1). These models leverage such biases to perform well on in-distribution (ID) evaluation data (Agrawal et al., 2018a). However, their poor performance on out- of-distribution data reveals that they merely rely on superficial features rather than capturing the truc causal relationships between inputs and targets.
Existing methods attempt to diminish a model's 's reliance on these shortcuts by taking one or both of two primary strategies: (a) by balancing the sample groups with and without spurious correlation, e.g. via data augmentation (Gokhale al., 2020) or sample synthesis (Chen al., 2020, 2022; Kolling et al., 2022a), and (b) by explicitly eliminating the impact of spurious correlations during model train- ing or inference (Huang et al., 2022; Lin et al., 2022; Pan et al., 2022). In the former approach, the identification of the unique set of spurious corre- lations each sample becomes essential curate augmented samples for achieving balance. Conse- quently, approaches that alleviate biases features or predictions, independent of the availability non-spurious data, are more desirable. Such meth-
1 Introduction
The success of multimodal models in various tasks has been attributed to their ability to rely spuri- ous correlations (or biases) present in the training data (Jabri al., 2016; Agrawal al., 2016; Zhang et al., 2016a; Goyal et al., 2017). An example of image bias VQA is when the model tends to look at prominent objects in the image rather than focusing on the object about which the question
'Our code is available at: https://githhu com/ Paidehi1gg/CausslIInffff

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ods also offer the additional advantage of being agnostic the specific dataset and task at hand.
2022). In TE-D, we leverage the rate-distortion function which controls the number of bits required to encode a set of vector representations (Chowd- hury and Chaturvedi, 2022). We minimize the rate- distortion function for a non-linear projection of the features extracted from a biased pretrained model, while simultaneously minimizing the cross-entropy loss of predicting from these projected features. This results in the loss of diverse information from the features and the retention of simple features that are also maximally predictive of the biased dataset. We treat these features as the confounder repre- sentations that stem from spurious correlations the dataset and compute the (unbiased) total effect of the input by taking the difference between the biased feature and its respective confounder.
Recent research on debiasing models has em- phasized the significance of causal theory (Zhang et al., 2021; Liu et al., 2022; Bahadori and Hecker- man, 2020) i.c., many spurious correlations origi- nate from confounding variables that induce non- causal dependencies between inputs and labels (Pearl et al., 2000). However, effectively identify- ing and representing biases that undermine predic- tion accuracy remains challenging task. Previous studies on multimodal models have utilized image features from early training stages as contextual bi- ases for multi-label image classification (Liu et al., 2022), or introduced unimodal training branches to mitigate spurious correlations in Visual Ques- tion Answering (VQA) (Niu et al., 2021). More- over, these approaches overlook biases stemming from multimodal interactions within their causal graphs. Hence, in this work, represent the bias as confounder variables that have a direct causal ef- fect on multimodal features and the corresponding predictions (see Fig. 2(a)). Spurious correlations represent the simplest predictive features that ex- plain biased datasets (Geirhos et al., 2020), thereby making them easily learnable by machine learn- ing models under limited representation capacity (Yang et al., 2022). We capitalize this notion to study a novel framework that combines informa- tion theory and causal graphs to learn confounder representations capable of capturing spurious fea- tures. We examine two approaches to learn the con- founder representations by imposing information loss on biased multimodal features i.e., (a) latent variable modeling using generative model and (b) rate-distortion minimization (Shannon, 1948). Subsequently, we utilize these confounders in our proposed debiasing methods, namely ATE-D and TE-D, leveraging the concepts of average treat- ment effect (Glymour et al., 2016) and total effect (Pearl, 2022) causal mechanisms, respectively.
We evaluate the proposed methods several multimodal tasks and along multiple dimensions i.c., in-distribution and -iitt---istribttiinn perfor- mance, efficiency, and robustness. Results show that these methods not only outperform baseline models with lower training overhead but also yield additional gains on top of unimodal debiasing meth- ods. In this work, we demonstrate the presence of multimodal biases and the need for multimodal debiasing along with the potential of confounder modeling via information loss in causal multimodal debiasing. Our contributions are as follows:
We present two methods, TE-D and ATE-D, that leverage auuaaly-mmtivatee information loss to learn confounder representations from biased fea- tures and utilize them to debias models.
Our methods remove multimodal biases and yield up to 2.2% and 2.5% gains over LXMERT (Tan and Bansal, 2019), on VQA-CP and GQA-OOD (Kervadec al., 2021) datasets respectively, and 0.7% gains on top of unimodal debiasing (Wen et al., 2021). Importantly, our methods exhibit su- perior parameter efficiency and reduced training time compared to existing debiasing methods.
We propose sufficiency score (A) for quantify- ing the reliance of models on spurious features. Results show that our methods improve robust- ness to spurious correlations in the dataset.
In ATE-D, we employ an autoencoder to recon- struct the biased features. The autoencoder projects these features into a dover-dimensional latent space, capturing latent features that act as substitutes for unobserved confounders (Huang et al., 2022). By clustering the learned confounder representations across the dataset, we construct dictionary of confounders. We subsequently perform backdoor adjustment based on the average treatment effect, utilizing feature reweighting (Kirichenko et al.,
We analyze the confounders learnt in ATE-D, TE-D and show that they encode dataset biases.
2 Related Work
Data Augmentation. Balancing data (Zhang et al., 2016b) can involve training a generative

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model for sample synthesis (Agarwal et al., 2020;
Sauer and Geiger, 2020). designing suitable data selection heuristics (Chen et al., 2020), or curat- ing balanned/coumttefaatuuall samples (Goyal et al., 2017; Gokhale et al., 2020; Kolling et al., 2022c). Human explanations can be used as additional train- ing signals to promote reasoning (Ying al., 2022; Wu and Mooney, 2019; Selvaraju et al., 2019). We debias models using existing biased data.
M
A
(a)
(b)
DeED. (M
M
M
(c)
Zmnc
Zyn.cc
Figure 2: Demonstration (a) our proposed causal graph for multimodal tasks, (b) Average Treatment Ef- feet (ATE), and (c) Total Rffecct (TE) on (a). Values in grey indicate the 'no-treatment" condition.
Inductive Bias in Model Architecture. Agrawal et al. (2018a) explicitly design inductive biases to prevent the model from relying on training priors. Clark et al. (2019); Cadene et al. (2019); Ramakr- ishnan et al. (2018) rely on a separate QA branch to weaken the language prior in VQA models via ad- versarial or multi-task learning. Wen al. (2021) contrastive loss remove unimodal biases for VQA. Peyrard al. (2022) discover invariant corre- lations in data across different training distributions to enable generalization.
used to represent causal relationships between random variables V. When the variable Q has an indirect effect on A through a variable M i.c. Q M A, the variable M is said be a mediator in the causal graph (see Fig. 2(a)). a variable C has a direct causal effect on both M and A, it is said to be a confounder.
Inductive Bias for Modeling Confounders. Kallus et al. (2018) recover latent confounders via low-rank matrix factorization and Sen et al. (2017) utilize low-dimensional variables for encoding con- founders. We low-dimensional features limit representational capacity for encoding confounders in multimodal data.
Causal Perspective for Multimodal Tasks. Mul- timodal models for tasks combining vision (V) and language (Q) often face the challenge of confound- ing variables, which introduce spurious features. Current approaches rooted in causal theory aim to mitigate direct unimodal effects. However, VQA example (Fig. 1) highlights limitation: models trained predominantly on centrally located objects struggle with queries about obscured object colors. Existing causal graphs for multimodal tasks fail to account for spurious correlations arising from such interactions. To address this, we propose a confounder C that influences both the mediator M and the answer A (Fig.2(a)). By modeling biases encoded in multimodal features confounder C, can eliminate biases using causal intervention. In order to debias VQA models, we adopt two causal mechanisms i.c., the Average Treatment Effect (ATE) and Total Effect (TE), which essen- tially refer to the same quantity but differ in how they deal with the confounder (VanderWeele,, 2015; Tang al., 2020a). In ATE, C is treated dis- tribution, and is sampled by assuming implicit causal association with the treatment M m. TE, C has an explicit causal association with the treatment M = m in each sample. We explore both in our work and discuss their theories below.
Causal Perspective. Lin et al. (2022) use causal intervention through backdoor adjustment (Gly- mour et al., 2016) to disentangle the biases for unsupervised salient object detection. Huang et al. (2022) use ATE to debias referring expression mod- els. Niu al. (2021) compute the Total Indirect Effect (TIE) of the multimodal branch omit the influence of unimodal branches. Veitch al. (2021) formalize counterfactual invariance and its relation to OOD performance. Liu et al. (2022) use features from early training as confounders and compute the Total Direct Effect (TDE) for multi-label image classification. We combine information theory and causal theory to leam confounders from biased rep- resentations and use them via ATE and TE causal mechanisms debias a model.
3 Causal Theory Preliminaries
In this section, discuss proposed causal graph for multimodal tasks and the two causal mechanisms relevant to our debiasing methods.
Average Treatment Effect. The aim of causal inference is to estimate the independent effect of an intervention on a treatment variable M on
Causal Graph. Causal graphs are directed acyclic graphs g = {V,E} where the edges

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Step
S purious
Non-spurious
what sport
L
Multimodal backbone
[Question]
Biased features (R)
Confounder (Dc) Dictionary
Step22
Debiased feature (P)
Multimodal backbone
ATE based
recalibration
Wat sport being played in this image?
Blased features(K))
fage
golf
Answer
head
Figure 3: illustration of our method ATE-D based on autoencoder- based confounder modeling and Average Treatment Effect causal mechanism (see Sec. 4. The confounders are modeled using autoencoder in Step I and biased features are recalibrated using confounders to get debiased features in Step 2.
outcome of interest A i.e. to estimate the conditional probability distribution P(A (M) where the do-operation implies the causal effect of M A. However, standard models are opti- mized to infer the observational conditional prob- ability M). the presence of confounders i.e. variables с C that affect both A and M, P(A[M) + P))) P(Aldo(M)) can be estimated using backdoor adjustment by control- ling for all values of the confounders C as:
founder. The direct effect of C. on M is elimi- nated by retaining the confounder both sides of the difference (see Fig. 2(c)). In practice, take the difference between feature representations of Am,Cm Ame.Cm i.e. Zm, Zin,ee respectively, eliminate the effect of Cm (see Sec. 4.2).
Debiasing Methods: ATE-D and TE-D
Kirichenko et al. (2022) show that machine- learning models learn spurious as well as non- spurious features when trained on a biased dataset, but over-rely on the former for making predictions. In Sec. 1, we discussed how confounder variables contribute to these spurious predictions. Further, Yang al. (2022) show empirically that deep mod- els preferentially encode dataset shorteuts under limited representation capacity. Indeed, neural nets are expected to trade-off between maximal com- pression of the learnt representations and maxi- mal fitting to the labels cnnformmtionnBottllene (Shwartz-Ziv and Tishby, 2022). Hence, we pro- pose information minimization. by limiting rep- resentation capacity via www-iieessionll vectors, to learn the bias/confounder features. Similar ap- proaches exist i.e. Kallus (2018) recover latent confounders by performing low-rank ma- trix factorization on high-dimensional data, and Sen et al. (2017) use low-dimensionaal variable to encode confounder. We propose two methods to learn and use confounder features for debias- ing: (a) latent variable modeling in ATE-D and (b) rate-distortion minimization in TE-D. In both approaches, the biased features are projected into Low-dimensional vectors through various mecha- nisms, limiting their representation capacity and promoting information minimization. Subsequent
P(Aldo(M)) = Ec-c(
(1)
This translates to an empirical sum over all possible values of the confounder in practice, also known as the average treatment effect (ATE) (see Fig. 2(b)). When the confounders are known and observed, the confounder values are selected using suitable heuristies (Pearl al., 2000). However, observing all confounders not always possible. Hence, model the variables that can be used as substitutes for the confounders via latent representations in autoencoders (Sen et al., 2017; Kallus et al., 201 8). Huang et al. (2022) use average treatment effect- based debiasing for the task of visual grounding by modeling confounders.
Total Effect. We need to isolate the causal effect of M - m on A, free from the influence of the confounders C. According to causal theory, the total effect (TE) of treatment M
A is,
TE = Am,Co. Ame.Cr.
(2)
where M m, M m* represent 'treatment" and 'no treatment' conditions respectively; Cm the confounder under treatment and Am,c... C.,. is the answer the presence of treatment as well as con-

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shortcut spat tennis
stop gradient
What sp sport being played this image?
R E)
Multimodal backbone
(7a)
Confounder (ae)
Debiased feature (zg")
Spurious
Answer
tennis
r-baa
Leee
Non-spurious
head
head
Figure 4: An illustration of our method TE-D based on Rate-Distortion & Total Effect causal mechanism (see Sec. 4.2). The biased features are used to learn confounder features guided by rate-distortion minimization and cross-entropy loss (Lee). The confounders are subtracted from the biased features to get debiased features.
sections further claborate these methods.
ous features and higher weights for more complex predictive features, alleviating the over-reliance on spurious features for prediction. For sequence of biased features r [r1, P...,,,... rk], we recali- brate each rj according their similarity with the confounders in D, i.e., the weight w; for ri is,
4.1 ATE-D: Deconfounding Using Average Treatment Effect
We follow a 2-step framework where we start with a pre-trained biased model, then (I) obtain the sub- stitute confounders from the latent variables of au- toencoder (Huang et al., 2022) and (2) use these confounders to debias the pretrained model using feature reweighing (Kirichenko et al., 2022).
- (en(Dg)
(Fen)((r).g)) (3) 9;ED,
Σ
where s(-) is the cosine-similarity function (see ATE-based recalibration in Fig. and see Appendix for explanation of recalibration as an instantiation of back-door adjustment).
We collect the biased features E R from biased model for all samples in the train- ing data and train autoencoder composed of dense layers (Fene, Fdee) encode them into lower dimension (see top, Fig. 3). The latent di- mensions of the generative model capture the most common biases the dataset and serve as a substi- tute for the confounders. We use a small-capacity network in order to capture the biases stemming from spurious correlations in the latent dimensions and avoid encoding the correct predictive features. Feney Fdec trained using the reconstruction loss Lrecon d( dRR, R), where d(.) is the Euclidean distance function. We model the substitute con- founders E Ò for R (: represents approximation) and cluster them to get dictionary Dz. which rep- resents the main elements of Ć' for efficient back- door adjustment (Eqn. 1).
Step 1:
- w. TTj R' Ir', 12.2..... r/]
(4)
The resulting debiased features R' are then used to replace R as shown in Fig.
4.2
TE-D: Debiasing Using Rate-Distortion & Total Effect
The rate- dit-oristotioon function R(Z, é) measures the minimum number of bits per vector required to encode the sequence Z = {2), 22, ...,,. } E Raxd such that the decoded vectors { can be recov- ered up precision e2 i.e.,
1
R(Z, log,
ne? ZZT)
(5)
Step 2: Kirichenko et al. (2022) show that non- spurious features can be emphasized in biased features by reweighing them using a balanced dataset. However, creating balanced data is non- trivial for complex tasks like VQA. To overcome this challenge, we instead create an instantiation of backdoor adjustment that reweighs biased fea- tures based on their similarity with the substitute
where !zz" is the estimate of covariance ma- trix for the Gaussian distribution (Chowdhury and Chaturvedi, 2022) and assuming that the vectors are i.i.d. samples from (0 1). Rate-distortion values higher for distribution with high vari- ance (diverse features). Hence, we minimize the rate-distortion learn confounder representations in TE-D. Our implementation illustrated in Fig. 4. Given a biased model with parameters 6, we first obtain the biased feature 2g. Then, we encode the
confounders (see bottom, Fig. 3). We hypothesize that this leads to lower weights for the simple spuri-

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2g into lower dimension to promote information loss, along with a classification head (Cor)) to encourage retaining predictiveness of the informa- tion present in the encodings, which we treat as the confounder representation Ae- Finally, we enforce rate-distortion minimization (R(ze; <)) on 2e for promoting the loss of complex feature information. We enforce stop gradient (see in Fig. 4) prior the encoder in order to prevent the training sig- nals for learning confounder representations from seeping into the parameters of the biased model.
in data to augment data based on style transfer. Gokhale et al. (2020) identify unimodal biases to augment multimodal datasets. However, such awareness may not be comprehensive or entirely precise. Consequently, the efficacy of data augmen- tation is contingent on the accuracy and complete- ness of the priori understanding of the biases un- derpinning the augmentation strategy. Conversely, methods that manipulate representation vectors di- rectly to remove biases, such as our proposed debi- asing techniques, extract spurious correlations from the data without requiring predefined assumptions about specific biases.
In order to isolate the causal effect of M, we need to cut off the link C' + M (see Fig. 2(c)). This can be achieved by computing the total effect (see Sec. 3) i.e., Am.c- Ams,cs where m and ms represent the treatment and no-treatment conditions respectively, while represents the confounder re- sulting from M m. We implement this at the feature level by representing Am.c with the biased features be and Ama.c with the confounder features Ze. Next, we take the difference of those features to secure =/ which represents the direct effect of M. i.e. 28 = 20 Ze- We further aid the debiasing process by enforeing contrastive loss between the three sets of features zg, Zes 2g as:
Scalability and cost implications. The creation of augmented datasets is often time-intensive as well as cost-intensive (Sauer and Geiger, 2020). The process demands domain expertise adeptly identify and apply augmentations (Tang et al., 2020b). This resource-intensive nature of data augmentation can curtail its applicability, especially when used for models that must adapt to a multitude of diverse, evolving sources of bias.
Automated discovery of spurious correlations, as performed in our proposed methods ATE-D and TE-D, advantageous over data augmentation when dataset biases are inadequately defined or in a state of perpetual flux. For instance, in numerous real-world applications, the dataset may harbor concealed or subtle biases that evade detection through manual inspection domain expertise. Similarly, in dynamic environments, dataset biases can undergo periodic shifts. As a result, pre-established augmentation strategies become unviable for such scenarios. The techniques proposed in this work can adapt to the changing characteristics of data within a black box, making them more useful.
cl .2)
Lcon = log
(6)
asge 26) + a.))
where s(.) is the cosine similarity function. The contrastive loss penalizes the model when the con- founder is correlated with the biased feature 28 and hence, promotes debiasing of the multimodal backbone itself. In summary, we jointly opti- mize the model for learning confounder represen- tations via C.ons, R R[Ze: <) and debiasing with the help of the learned confounders via Leons Loe i.c., Odecom = argmingCrom Live + Leonf +aR Ze, e). where a is the weight factor for rate-distortion loss.
4.3 Causal Debiasing vs. Data Augmentation
Another research thread aims to uncover coher- ent data subsets on which machine learning models exhibit subpar performance, such as the approach introduced in Domino (Eyuboglu et al., 2021). When these underperforming slices are accurately identified and labeled, it offers an opportunity to enhance model robustness by either updating the training dataset or employing optimization tech- niques designed to handle systematic performance issues in these slices. While this method aligns with our objective of improving the identification of sys- tematic biases, slice discovery approaches achieve
Data augmentation is an effective and popular method for enhancing model robustness (Puli et al., 2023; Gokhale et al., 2020; Chen et al., 2020), how- ever, it presents certain constraints, particularly when employed in the context of debiasing within VQA models, such as:
Dependency on prior knowledge. Data augmen- tation typically hinges on pre-existing knowledge of potential biases within the dataset. For instance, Mikolajezyk-Bann Bareta (2023) use knowledge of bi- ases i.e. the presence of shape and texture bias

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it from data perspective and require ground truth labels, whereas we take a distinct basture-based approach that does not rely on the ground truth.
to prediction. The certainty of predictions is determined by the Kullback-Leibler (KL) diver- gence between the predicted output distribution and a uniform distribution across all samples in the group (Ying et al., 2022). We define the sufficiency score (A) as the percentage of the model's certainty that can be attributed to the spurious component of the input in making a prediction. For data sample (æt, y), where the input æ consists of the spurious feature xt and the remaining context x", i.e., x [a* x"",, we compute the sufficiency À as:
5 Measuring Sufficiency & Necessity of Spurious Features in Multimodal Tasks
OOD generalization accuracies indicate the model's ability learn causal relationships be- tween inputs and labels (Veitch et al., 2021). An- other approach to assess causal learning is by ex- amining the models' invariance to spurious fea- tures in the dataset. Joshi et al. (2022) categorize spurious features into (a) Type Features that are neither necessary
A (L(f(ul==)) E (L(S(un(Z:))UU)
(7)
sufficient for predicting the label e.g., 'person' (visual feature) when the VQA question is "How many trees are in the picture?" (see left, Fig. 5) (b) Type 2 Features that are nec- essary but not sufficient to make predictions c.g., the feature "Is the man" (see right, Fig. 5). When a model consistently answers "yes" to all "Is the man..." questions regardless of the image, it is con- sidered to exhibit spurious behavior. We employ this framework to analyze debiasing methods in our experiments.
Here, U(.) represents the uniform distribution, f(-) denotes the trained model, and G is a group of samples. A reliable debiasing technique should reduce the sufficiency of spurious features. In the of the multimodal Visual Question Answering (VQA) task, where 2; (gi, 0;), evaluate suf- ficiency of Type features that arise in the textual modality d-- To compute (yslgf, vi), we mask of in the query before feeding it as input to f().
Experiment Setup
Type
Type 2
Datasets. We evaluate the performance of our methods in both in-distribution (ID) and out-of- distribution (OOD) settings on multiple multimodal tasks, including VQA-CP (Agrawal et al., 2018b), GQA (Hudson and Manning, 2019), GQA-OOD (Kervadec al., 2021), and NLVR2 (Suhr et al.. 2019) datasets. To further assess robustness in the presence of language and vision biases, we create the IVQA-CP test set by replacing the original im- ages in the VQA-CP test set with counterfactual images from IV-VQA (Agarwal et al., 2020). These IV-VQA images have been edited to remove irrele- vant objects while preserving the original ground truth label (details in Appendix).
Q. How many trees are this picture?
Q. Is the man wearing plain tie?
not necessary and not sufficient
necessary
but suffffiintt
Figure 5: Types of spurious features (red) in VQA based necessity and sufficiency.
Necessity. To assess the robustness of models to Type 1 features, we compare their performance on samples with and without a specific Type feature. In unbiased model, the absence of this feature should have impact performance. However, biased model tends rely it due spurious correlations that confound the features and labels. An effective debiasing method should render the model invariant to such features. Type I features predominantly arise from the image in multimodal tasks, as depicted in Fig. 5. Therefore, we evaluate the necessity of these features using counterfactual images (Agarwal et al., 2020) (refer to Sec.6).
Architectures. We use the LXMERT (Tan and Bansal, 2019) model as our baseline and imple- ment our methods TE-D and ATE-D on top of LXMERT for all datasets. Since VQA-CP a re- organization of the VQA v2 dataset and LXMERT is pretrained on VQA v2, initializing pretrained LXMERT model for finetuning VQA-CP leads to data leakage and an unreasonable increase in accuracy. Hence, we train LXMERT-based models, baselines from scratch our experiments and are not comparable to numbers in Wen et al. (2021); Gokhale et al. (2020) affected by data leakage.
Sufficiency. To assess the robustness of models to Type features, we propose a new metric for measuring the sufficiency of feature in relation

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VQA-CP
IVQA-CP
Additional
Overall
YesoNo
Num
other
Overall
Yes/No
Num
other
#MFLOPS
LXMERT (Tan und Bunsal, 2019)
41.2
44.1
13.9
47.2
35.0
43.3
12.7
36.8
+ IRM (Peyrard et al., 2022)
42.7
44.1
15.2
49.5
36.5
43.2
12.8
39.3
+ ATE-D (ours)
42.2
43.6
14.6
49.0
35.8
42.9
13.2
38.2
0.7
TE-D (ours)
43.4
48.3
14.4
36.7
46.5
12.8
38.1
8.8
CD-VQA (Kolling et al., 2022b)
42.1
42.7
14.8
49.3
36.3
44.7
12.9
38.7
+ GenB (Cho al., 2023)
52.8
67.3
29.8
49.7
41.3
50.7
16.7
39.4
50,2
D. VQA, (Wen et al., 2021)
43.9
47.5
15.7
49.8
37.3
45.8
13,9
39.2
18.9
D. VQA; ATE-D
43.9
47.2
15.9
49.9
37.4
45.7
13,9
39.3
19.6
D-VQA, + TE-D
44.6
47.8
15.7
50.8
37.8
46.2
13,9
40.1
27.7
D-VQA
52.4
63.5
29.7
51.8
44.6
62.9
26.4
39.9
25.0
Table I: Accuracy results on the VQA-CP (Agrawal et al., 20) and IVQA-CP (Agarwal et al., 2020) test sets.
Higher better. Column 'Additional MFLOPs' represents extra MFLOPS introduced by each method over the LXMERT backbone, We report results using LXMERT model free of the data leakage issue.
Robustness to Type spuricus features
Most frequent answer by question type
102
VGJA-3 train (Crouns Tnsh)
(Blas Prediction)
Suffiiieccy
LIMERT ATTEDD
0.0
boseball
IAAA
LONERT
% of oa answers
tanale
yes
DVDALT + D
na
right left
22
white
white
red
are these
the person
Figure 6: Using our sufficiency metric (A, lower bet- ter), we show that our debiased models rely less on Type 2 spurious features than baseline models.
30
what color
many
which
Figure 7: Most frequent answer by question type in
7 Results & Discussion
VQA-CP train, test, and bias predictions from TE-D.
In this section, we discuss the results from the eval- uation of our methods for generalization, robust- ness, effectiveness, and efficiency, and analysis of the learned confounder representations.
GenB (Cho al., 2023) achieves state-of-the-an results on top of LXMERT by using ensembles of distilled models but compromises on efficiency. We see 1.8% and 2.3% gains in GQA-OOD ac- curacy with ATE-D and TE-D over the LXMERT baseline (see Tab. 2). The GQA-OOD dataset is fur- ther divided into OOD-Head and OOD-Tail splits which represent the samples containing answers from the head and tail of the answer distributions respectively; our methods achieve improvements both groups. These gains are obtained along with gains in-distribution (ID) accuracy on GQA (see Tab. 2). Additionally, we see 0,4%, 0.5% gains with ATE-D, TE-D respectively on NLVR2, an ID evaluation setting for visual entailment task (see Tab. 3). This shows that our methods do not hurt in-distribution performance and task-agnostic.
7.1 Does causal debiasing help improve out-of-distribution generalization?
We evaluate the effect of causal debiasing on im- proving generalization by evaluating our methods on three multimodal datasets. First, we observe that our methods, ATE-D and TE-D, demonstrate I % and 2.2% gains over LXMERT on the VQA- CP test set (see Tab. 1). TE-D improves the ac- curacy of Yes/No category by 4.2% which has higher bias presence as seen in Fig. 7 and outper- forms D-VQA,. y. state-of-art unimodal debiasing method VQA (feature perspective only), by 0.8% (p=0.04) the Yes/No category, while the latter achieves better overall accuracy on VQA- CP. However, our methods can be used to debias features in any backbone and task, in contrast to D- vQA, y that has been designed for VQA. Moreover, D-VQA trains debiased model from scratch while TE-D debiases biased model with few epochs of fine-tuning (see efficiency in Sec. 7.4).
7.2 What kind of biases are captured by confounder representations?
ATE-D. First, we find that up- spiieight features similar to the confounders learned in ATE-D, opposed to down-weighting (see Sec. 4.1), signifi- cantly hurts OOD accuracy implying that the con- founder representations indeed encode spurious correlations. Next, we train a non-linear probe on the confounder representations for the VQA task. The accuracy of this probe is 25% and the distribu-
"Statistical significance is computed with 100K samples using hootstrap (Noreen, 1989; Tibshirani and Efron, 1993). All other guins are statistically significant.

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tion of predicted answers of this probe has lower entropy than that of the predicted answer distribu- tion from unbiased features. Lower entropy sug- gests higher bias in the semantic concepts encoded in the confounders.
GQA
GQA OOD
ID
Tail
Head
All
LXMERT and Bansal, 2019)
49.8
57.7
54.6
VILLA (Gan et al., 2020)
49,9
57.2
54.5
MMANGO al., 2020)
54.9
+X C-C (Jiang al., 2021)
49.9
57.5
55.6
+ATE-D (ours)
60.0
50.8
59.9
56.4
(ours)
51.4
60.1
TE-D. The bias representations in TE-D capture the most prominent input-output biases in the VQA- CP train set, accounting for answers in 0.34% of the answer vocabulary but covering approximately 67% of the train questions. The classifier head con- nected to these bias representations achieves 28% accuracy on the VQA-CP test set, while the overall causal model accuracy is 44%. The most frequent answers predicted by this classifier head on the VQA-CP test set align with those in the VQA-CP train set, showing that the captured confounders effectively represent dataset biases (see Fig. 7).
Table 2: Accuracy results GQA ID and OOD datasets for various debiasing methods. Higher is better.
Acc.
Cons.
LXMERT (Tan and Bansal, 2019)
74.5
39.4
+ATE-D (ours)
74,9
39.9
+TE-D (ours)
75.0
39.6
Table 3: Accuracy (Acc.) and consistency (Cons.) sults on NLVR2 ID test set. Higher is better.
of these models on spurious features for making predictions. TE-D and D-VQA, achieve similar sufficiency scores, suggesting that they are equally effective at improving robustness by giving more importance to the context. TE-D achieves lower À than ATE-D which aligns with its larger accuracy gains (see Tab. 1).
7.3 Does causal debiasing improve robustness to spurious features?
Type Spurious Features. In Sec. 5, we discuss Type spurious features that are irrelevant to the target output. Our IVQA-CP test set (Sec. 6) shares question annotations with VQA-CP but has images edited to remove irrelevant objects (Agarwal et al., 2020). Models trained on VQA-CP are evaluated on this dataset, allowing assessment of their robust- ness to spurious features. The LXMERT baseline shows a significant drop from 41.2% to 35.0% on IVQA-CP (Tab. 1), indicating the evaluation's 's chal- lenging nature. Our methods, ATE-D and TE-D, achieve 0.8% and 1.7% improvements respectively over LXMERT on the IVQA-CP test set, enhancing robustness to Type 1 features. D-V performs explicit visual debiasing and hence, exhibits the highest robustness to Type features in IVQA-CP.
7.4
Is cross-modal debiasing more effective and efficient than unimodal debiasing?
D-VQAy outperforms cross-modal debiasing Table 1, but when D-VQA, is treated as the bi- ased model in TE-D, additional improvements of 0.7% (p=0.03) are achieved, indicating that cross- modal interactions contribute to bias not addressed by unimodal debiasiin. Cross-mode feature- based confounders effectively mitigate biases involving multiple modalities. Our causal debiasing methods demonstrate higher efficiency compared to D-VQA, with ATE-D adding 0.7 MFLOPS and TE-D adding 3% additional parameters and 8.8 MFLOPS to LXMERT. In contrast, D-VQA adds 5% additional parameters and 18.9 MFLOPS during training, re- quiring more time as it is trained from scratch. Ef- ficiency results for GQA and NLVR are the same as those reported for VQA.
Type 2 Spurious Features. A prominent source of Type 2 spurious features in VQA is the first few words of a question, as seen in Fig. 5. We introduce the sufficiency score (A, see Eqn. 7) to understand whether debiasing methods truly improve the ro- bustness of models to such spurious features. We select two question types i.e. questions starting with "Are these" and "Is this person", which are strongly biased in the training set of VQA-CP, and compute the sufficiency of the phrases for model predictions by masking the remaining question (see Sec 5). As shown Fig. 6, we find that causal de- biasing methods lower the sufficiency score of the spurious feature for both of these question types, suggesting that they indeed alleviate the reliance
Conclusion
We propose ATE-D and TE-D to mitigate biases in models by imposing causally-driven informa- tion loss on biased features to Iearn confounders. Experimental results across various multimodal tasks, datasets, and backbones demonstrate that the learned confounders capture biases successfully, and our methods effectively climinate biases from both unimodal and multimodal interactions.

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9 Limitations
answering models. In Proceedings of the 2016 Con- ference on Empirical Methods in Natural Language Processing, pages 1955-1960, Austin, Texas. Associ- ation for Computational Linguistics.
While we evaluate robustness to spurious features, we do so on specific question types for Type 2 features and specific Type features (irrelevant objects in the image). Getting an all-inclusive ro- bustness metric for evaluating debiasing methods would be insightful. Approaches that debias using data augmentation or sample balancing, although cumbersome, are more effective than feature-basce debiasing approaches, including those proposed in our paper. More analysis is required to understand how the merits of sample-perspective and feature- perspective methods can be merged efficiently.
Aishwarya Agrawal, Dhruv Batra, Devi Parikh, and Aniruddha Kembhavi. 20. Don't just assume; look and answer: Overcoming priors for visual ques- tion answering. In IEEE Conference on Computer Vision and Pattern Recognition ( (CVPR).
Aishwarya Agrawal, Dhruv Batra, Devi Parikh, and Aniruddha Kembhavi. 20. Don't tt just assume; look and answer: Overcoming priors for visual ques- tion answering. In Proceedings of the IEEE ference on computer vision and pattern recognition, pages 4971-4980.
10 Broader Impact
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Mar- garet Mitchell, Dhruv Batra, C. Lawrence Zitnick, and Devi Parikh. 2015. VQA: Visual Question An- swering. In International Conference Computer Vision (ICCV).
In this work, the biases that we try to mitigate stem from the spurious correlations present in the dataset that lead drop in performance in OOD settings. This helps models learn causal associa- tions between inputs and targets and thus brings them closer to real-world deployment as it helps mitigate unethical use of these models. However, vision-language models may encode other societal stereotypes and biases present in the data they are trained on and also introduce new ones. VL mod- els explored in this paper are not immune these issues. We are hopeful that our focus on modeling biases and alleviating them is a step towards more inclusive models.
Mohammad Taha Bahadori and David Heckerman. 2020. Debiasing concept-based explanations with causal analysis. In International Conference on Learning Reprexentations.
Remi Cadene, Corentin Dancette, Matthieu Cord, Devi Parikh, et al. 2019. Rubi: Reducing unimodal biases for visual question answering. Advances in Newral Information Processing Systems, 32:841-854
Long Chen, Xin Yan, Jun Xiao, Hanwang Zhang, Shil- iang Pu, and Yueting Zhuang. 2020. Counterfactual samples synthesizing for robust visual question an- swering. CVPR.
Acknowledgments
Long Chen, Yuhang Zheng, and Jun Xiao. 2022. Re- thinking data augmentation for robust visual question answering. In Computer Vision ECCV 2022 17th
We thank Peter Hase, Zhuofan Ying, and Jaemin Cho for their useful insights about this work, and the reviewers of this paper for their help- ful feedback. This work was supported by ARO W911NFF1 10220, DARPA MCS N66001-19-2- 4031, ONR N00014-23-1-2356. DARPA ECOLE Program No. HR001 12390060. The views, opin- ions, and/or findings contained in this article are those of the authors and not of the funding agency.
European Conference, Tel Aviv, Israel, October 23- 27, 2022, Proceedings. Part XXXVI, volume 13696 of Lecture Notes in Computer Science, pages 95-112. Springer.
Jac Won Cho, Dong-Jin Kim, Hyeonggon Ryu, and In So Kweon. 2023. Generative bias for robust visual question answering. In Proceedings of the IEFF/VV Conference on Computer Vision and Pattern Recog- nition (CVPR).
Somnath Basu Roy Chowdhury and Snigdha Chaturvedi. 2022. Learning fair representations via rate-distortion maximization. arXiv preprint urXiv:2202.000:
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In order to debias VQA models, we adopt two causal mechanisms i.c., the Average Treatment Ef- feet (ATE) and Total Effect (TE), which essentially refer to the same effect but differ in how they deal with the confounder (VanderWeele, 2015; Tang et al., 2020a). In ATE, C' is treated as distribu- tion, and e is sampled without assuming a causal association with the treatment M In TE, is causally associated with the treatment M m in each sample. We explore both mechanisms in our experiments and discuss their theories below.
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Average Treatment Effect. The aim of causal inference is to estimate the independent effect of an intervention treatment variable M out- come of interest A i.c. to estimate the conditional probability distribution P(Aldo(M)) However, standard models are optimized infer the observa- tional conditional probability P(A[M) and in the presence of confounders i.e. variables c E C' that affect both A and M
A Causal Theory Preliminaries
In this section, we discuss our proposed causal graph for multimodal tasks and the two causal mechanisms relevant to our debiasing methods.
Causal Graph. Causal graphs are directed acyclic graphs G = -V, where the edges & are used to represent causal relationships between ran- dom variables V. An example is shown in Fig. 2(a). where M has a direct effect on A. When the vari- able Q has an indirect effect on A through a vari- able M.c. Q - M + A, the variable M is said to be a mediator in the causal graph. If a variable C has direct causal effect on both M and A, it is said to be confounder.
(8) where the do-operation implics the causal effect of M + A. P( ((Aldo(M) can be estimated using backdoor adjustment by controlling for all values of the confounders c E C, i.e.,
P(AIM) + P(Aldo(M))
P(A]do(M)) E=-c(P((|MM,))
(9)
Causal Perspective for Multimodal Tasks. Models developed for multimodal tasks are de- signed to use the combined data stream of vision (V) and language (Q) for solving the task. How- ever, the unimodal data variables may act as con- founders and give rise to spurious features in the model e.g. via Q + M + A. Existing ap- proaches that leverage causal theory for debiasing multimodal models aim to eliminate the direct uni- modal effects. However, consider the VQA exam- ple in Fig. 1. A potential spurious correlation that may lead incorrect predictions from models on similar examples that most training instances where the question asks the color of an object, the object is present in the center of the image. Spuri- ous correlations arising from such multimodal in- teractions are ignored in existing causal graphs for multimodal tasks. Hence, we propose to model the spurious correlation the mediator M and the answer A (see Fig. 2(a)). This allows us model the biases encoded in the multimodal features as confounder C and climinate the bias using causal intervention.
This translates to an empirical sum over all possible values of the confounder in practice, also known as average treatment effect (ATE) (see Fig. 2(b)). When the confounders are known and observed, the confounder values are selected using suitable rules and heuristics (Pearl et al., 2000).
Total Effect. We need to isolate the causal effect of M = m on A, free from the influence of the confounders C. According to causal theory, the total effect (TE) of treatment M = on A can be computed as,
TE = Am,Cm - Am-,Cm
(10)
where M = represents the "no treatment" con- dition and C... represents the confounder under the treatment condition i.e M m. By retaining the confounder in both sides of the difference, we elim- inate the direct effect of Cm on M (see Fig. 2(c)).
confounder C that affects
A.1 ATE-D
Step-2 of ATE-D:

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Sufficieney. In order to evaluate the robustness sufficiency of type 2 features, we propose a novel metric for quantifying the sufficiency of a feature towards a prediction. We define the certainty of predictions as the KL divergence between the pre- dicted output distribution and uniform distribution (11) across all samples in the group (Ying et al., 2022), We define the sufficiency score (X) the certainty of model's prediction when only the non-spurious Inspired by feature reweighing (Kirichenko et al.. 2022), we instantiate backdoor adjustment by re- calibrating ri based on confounder similarity i.e., of )) (see Fig. 2(b)) as,
d())d)(Q).do(VV) P(A)do(M))
Ec(P(A|M,C) = E (PA|M,
(12) features are the input to the model. Further, in or- der to make this metric comparable across models, we normalize this with the certainty of the model's predictions when the complete sample i.e., spuri- ous as well as non-spurious features, is the input to the model. This results in metric that repre- sents the percentage of certainty of the model that can be attributed to the non-spurious component of the input. For data sample (x, y). let the in- put be comprised of the spurious feature x² and the remaining context af i.e. x [=f;=]]. The sufficiency À is computed as follows:
PMA 6))
(13)
See appendix of Huang al. (2022) for complete proof. In our analysis, we instantiate f()) as the cosine similarity function in s(),, discussed in Sec 4.1.
B Analysis
While OOD generalization accuracies are indica- tive of the model learning causal relationships be- tween the inputs and labels, another way to probe causal learning to investigate if the models are robust to spurious features present in the dataset. order to evaluate this, in this section, we discuss an analysis framework for probing the behavior of models toward spurious features and propose a new metric for evaluation. Joshi et al. (2022) define the probability of necessity (PN) of feature X; for predicting the label Y as the probability that the ground truth label Y changes when the feature X; is changed. Similarly, they define the probability of sufficiency (PS) of a feature X; for predicting the label Y as the probability that setting X; = x; in a sample where X; + D; is absent changes its ground truth label Y. Based on this framework, spurious features are categorized into (a) low PN, low PS features: These features are irrelevant to the ground truth label e.g., person in the image when the VQA question is "How many trees are in the picture?" (see Fig. 5) (b) High PN, low PS features: These features are necessary but not sufficient to make predictions i.e. the model should rely on other fea- tures in their presence. For instance, when a model always answers "yes" to all questions starting with "Is the man."" irrespective of the image, the model is biased towards the feature "Is the man."" (sce Fig. 5). Henceforth, we refer the low PS, low PS, and high PN, low PS features as Type and Type 2 features respectively. We use this frame- work to analyze the various debiasing methods in our experiments.
Σ KL(f(L)==;1IU) Di=1 (L((((n(<:](U)
(14)
where U(-) represents the uniform distribution, f(-) is the trained model, and G is group of sam- ples. A good debiasing technique should increase the sufficiency of non-spurious features. For the multimodal VQA task where x; (g:, u,), we focus on the type 2 features emerging in the text modality g-- To compute f | 04), we mask o in the query before sending as input to f(.).
C Experiment Setup
C.1 Datasets
VQA-CP (Agrawal al., 2018a): It is a re- organization the VQAv2 (Antol et al., 2015) such that the distribution of question type- answer correlation is different between the train and test splits. This evaluation helps demonstrate the method's s ability to debias in a setting where language bias is dominant.
VQA-CP + IV-VQA: We evaluate it on a new version of the VQA-CP test set where we re- place the image in each sample with their in- variant counterparts from the IV-VQA dataset from (Agarwal et al., 2020). IV-VQA dataset has images replaced with their edited version obtained after removing irrelevant objects in a way that the predicted answer does not change.

---

Hyperparameter
LXMERT
ATE
TE
Learning Rate
5e-5
5e-5
5c-5
Epochs
20
5
Max Gradient Norm
1.0
1.0
1.0
Weight Decay
0.0
0.01
0.01
Batch Size
32
32
32
Max Length
128
128
128
Warmup Ratio
0.1
0.1
0.1
I.R Decay
Linear
Linear
Linear
Optimizer
AdamW
A
Adam
Bias dimension factor
Confounder dictionary size
Table 4: Training hyperparameters for different models trained on the VQA-CP dataset.
This adds another layer hardness to the benchmark along the image dimension. This evaluation helps demonstrate the method's ability to debias in setting where both lan- guage and vision biases are dominant.
D Results
D.1 Analysis of confounder features
We compare the most frequent answer in the VQA- CP training and test sets with those from the predic- tions of the bias classifier head TE-D Fig. 7. As discussed Sec.5, the predictions from bias classifier head closely track the distribution of an- swers in VQA-CP training set, even though the VQA-CP test set distribution is significantly differ- ent from VQA-CP train. This shows that the con- founder representations indeed capture the strong priors present in training set.
GQA(Hudson and Manning, 2019), GQA- OOD(Kervadec et al., 2021):
GQA evaluation helps measure visual rea- soning as well compositional question- answering abilitics. GQA-OOD is re- organization of the GQA dataset that intro- duces distribution shift in validation and test sets based on question type similar VQA- CP.
Explanation and proof for biases stemming from multimodal interactions Multimodal mod- els have been known to be brittle linguistic biases [1] and visual biases [2]. In this work, we demon- strate the presence multimodal biases and the need for removing those biases from multimodal features. (Proof) Many existing debiasing methods focus removing each unimodal bias (e.g., lin- guistic) from multimodal features independently of the other unimodal biases (e.g., visual). How- ever, [3] suggest that the biases can stem from multimodal interactions as well; they perform se- mantic edits on images in VQA (I-VQA dataset) that should not affect the ground truth, and show that the answers from multimodal models change in response to these invariant edits. (Existing Meth- ods) Indeed, methods like D-VQA [2] leave large room for improvement in terms of performance the IVQA-CP dataset [see Lines] that designed to for multimodal biases. (Our Approach) We formalize this phenomenon through the causal graph proposed in our paper in Fig. 2, where we explicitly model the confounders
NLVR2 (Suhr et al., 2019): It helps the gener- alization to multimodal tasks other than ques- tion answering. helps evaluate reasoning abilities about sets of objects, comparisons, and spatial relations.
All our experiments run with single seed value.
Baselines. We use D (feature perspective only) (Wen et al., 2021) based on LXMERT as the baseline for experiments with VQA-CP and train from scratch due to the aforementioned rea- sons. We also present results from D-VQA (both feature & sample perspective) for comparison, how- ever, note that methods using data balancing are not comparable to causal debiasing methods (sce Sec. l).
show in Table

---

VQA-CP
IVQA-CP
Additional
Overall
YesoNo
Num
other
Overall
YesoNo
Num
other
#MFLOPS
LXMERT (Tan and Bansul, 2019)
41.2
44.1
13.9
47.2
35.0
43.3
12.7
36.8
+ IRM (Peyrard ct al., 2022)
42.7
44.1
15.2
49.5
36.5
43.2
12.8
39.3
+ ATE-D (ours)
42.2
43.6
14.6
49.0
35.8
42.9
13.2
38.2
0.7
+ TE-D (ours)
43.4
48.3
14.4
48.8
36.7
46.5
12.8
38.1
8.8
D-VQA, (Wen al.,, 2021)
15.7
49.8
37.3
45.8
13.9
39.2
18.9
D-VQA, ATE-D
43.9
47.2
15.9
49,9
37.4
45.7
13.9
39.3
19.6
D-VQA, TE-D
44.6
47.8
15.7
50.8
37.8
46.2
13.9
40.1
27.7
D-VQA
52.4
65.5
29.7
51.8
44.6
62.9
26.4
39.9
25.0
Table 5: Accuracy results on the VQA-CP (Agrawal al., 2018a)and IVQA-CP (Agarwal al., 2020) test sets, Higher is better. Column 'Additional MFLOPs' represents extra MFLOPS introduced by each method over the LXMERT backbone. We report results using a LXMERT model free of the data leakage issue.
that affect the variable connecting multimodal rep- resentation (M) and the outcome (A). The unimodal biases implicitly modeled via the multimodal variable (Q->M->A, V->M->A). (Example) We demonstrate an example of this phenomenon in Fig. 1, where D-VQA fails to answer a question from the IVQA-CP test set correctly, and our pro- posed method, TE-D, is able to answer correctly because of multimodal debiasing. (Empirical Re- sults) Additionally, we show improvements on top of unimodal debiasing methods like DVQA(f) with multimodal debiasing approach (see rows 6,7 in Table 1). Our goal in this work is demonstrate the presence of multimodal biases and the need for multimodal debiasing along with the potential of confounder modeling via information loss in causal multimodal debiasing and our results support this claim.


Reviews:
[{"review_id": "r5x3xzRA5C", "reviewer": "Reviewer_kR71", "summary": "", "questions": "See weakness.", "limitations": "", "rating": 3, "confidence": 5, "soundness": 2, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for their feedback. Please see our response below:\n\n- **Explanation and proof for biases stemming from multimodal interactions**: Multimodal models have been known to be brittle to linguistic biases [1] and visual biases [2]. In this work, we demonstrate the presence of multimodal biases and the need for removing those biases from multimodal features. **(Proof)** Many existing debiasing methods focus on removing each unimodal bias (e.g., linguistic) from multimodal features independently of the other unimodal biases (e.g., visual). However, [3] suggest that the biases can stem from multimodal interactions as well; they perform semantic edits on images in VQA (I-VQA dataset) that should not affect the ground truth, and show that the answers from multimodal models change in response to these invariant edits. **(Existing Methods)** Indeed, methods like D-VQA [2] leave large room for improvement in terms of performance on the IVQA-CP dataset [see Lines] that are designed to test for multimodal biases, as we show in Table 1. **(Our Approach)** We formalize this phenomenon through the causal graph proposed in our paper in Fig. 2, where we explicitly model the confounders that affect the variable connecting multimodal representation (M) and the outcome (A). The unimodal biases are implicitly modeled via the multimodal variable (Q->M->A, V->M->A). **(Example)** We demonstrate an example of this phenomenon in Fig. 1, where D-VQA fails to answer a question from the IVQA-CP test set correctly, and our proposed method, TE-D, is able to answer correctly because of multimodal debiasing. **(Empirical Results)** Additionally, we show improvements on top of unimodal debiasing methods like DVQA(f) with our multimodal debiasing approach (see rows 6,7 in Table 1). Our goal in this work is to demonstrate the presence of multimodal biases and the need for multimodal debiasing along with the potential of confounder modeling via information loss in causal multimodal debiasing and our results support this claim. We will emphasize this explanation in the Introduction section of our paper in its updated version.\n\n\n- **The proposed problem and technologies are not novel in debiasing learning and multimodal learning, such as the causal graph, debiasing, the Average Treatment Effect, and the Total Effect**: Yes, the task of debiasing in multimodal learning is not novel. In fact, it is a widely studied problem with significant implications for real-world deployment of such models. Moreover, technologies like causal graphs, Average Treatment Effect, Total effect are core tenets of causal theory and have been widely used in debiasing papers that leverage causal theory [6,7]. We do not make any claims about the novelty of the task or the aforementioned technologies, and accordingly, we place them under the section named ‘Preliminaries’. Our paper is motivated by the importance of the debiasing task and the simplicity of the causal framework, and our method addresses limitations of existing debiasing methods. Our novelty lies in the fact that we use information theory to learn confounders and we remove biases caused by multimodal interactions (Lines 139-142) by instantiating causal debiasing methods. To our knowledge, we are the first to propose this approach.\n\n\n\n- **Error in Figure 1**: Thanks for pointing it out. We’ll replace the word “blue” in the first line of the second image with a “red”.\n\n\n- **Why do the existing methods remove unimodal biases whereas the proposed method removes biases arising from cross-modal interactions? The authors should explain it by combining Figure 1**: Thank you for the suggestion. Please see our answer to question 1 as a response to this question. Let us know if our answer requires further clarification.\n \n- **Why variables can be used as substitutes for the confounders via latent representations in autoencoders?**: Confounders are variables that affect both the input and the outcome without creating a causal link between them [4] (see path M->C->A in Fig 3). These undesirable correlations are caused by biases in the dataset which create a non-causal link between input and outcome (for example, the answer to most questions starting with “What sport” is “tennis” in the dataset. But “What sport” and “tennis” are not causally correlated). Past work [5] has shown that deep models preferentially encode dataset shortcuts under limited representation capacity and confounders can be modeled as low-dimensional vectors [Lines 179-182, 295-299]. We use this principle in our proposed methods to learn confounders (or dataset biases) by imposing information loss on multimodal features. In ATE-D, the latent representations of an autoencoder are a result of compressing the original representations into low-dimensional vectors and are thereby expected to capture biases that represent the confounders [Lines 313-316]. Intuitively, confounders are biases which have low variance and high bias and feature compression helps capture biases.\n\n\n[1] Goyal, Yash, et al. \"Making the v in vqa matter: Elevating the role of image understanding in visual question answering.\" Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.\n\n[2] Wen, Zhiquan, et al. \"Debiased visual question answering from feature and sample perspectives.\" Advances in Neural Information Processing Systems 34 (2021): 3784-3796.\n\n[3] Agarwal, Vedika, Rakshith Shetty, and Mario Fritz. \"Towards causal vqa: Revealing and reducing spurious correlations by invariant and covariant semantic editing.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020.\n\n[4] VanderWeele, Tyler J., and Ilya Shpitser. \"On the definition of a confounder.\" Annals of statistics 41.1 (2013): 196.\n\n[5] Yang, Wanqian, et al. \"Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers.\" Advances in Neural Information Processing Systems 35 (2022): 20351-20365.\n\n[6] Mohammad Taha Bahadori and David Heckerman. 2020. Debiasing concept-based explanations with causal analysis.In International Conference on Learning Representations\n\n[7] Wenkai Zhang,Hongyu Lin,Xianpei Han,and Le Sun. 2021. De-biasing distantly supervised named entity recognition via causal intervention. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume1: Long Papers), pages 4803–4813, Online. Association for Computational Linguistics.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. This paper is well-organized.\n2. The authors propose two methods, TE-D and ATE-D, that leverage causally-motivated information loss to learn confounder representations from biased features.\n3. The authors propose a sufficiency score to quantify the reliance of models on spurious features.", "reasons_to_reject": "1. The authors point out that these existing approaches overlook biases stemming from multimodal interactions within their causal graphs. It is not convincing because it lacks explanations and proof.\n2. The proposed problem and technologies are not novel in debiasing learning and multimodal learning, such as the causal graph, debiasing, the Average Treatment Effect, and the Total Effect. \n3. There seems to be an error in Figure 1. \n4. Why do the existing methods remove unimodal biases whereas the proposed method removes biases arising from cross-modal interactions? The authors should explain it by combining Figure 1.\n5. Why variables can be used as substitutes for the confounders via latent representations in autoencoders?", "questions_to_authors": "See weakness.", "paper_topic_and_main_contributions": "This paper focuses on debiasing multimodal learning and proposes ATE-D and TE-D to mitigate biases in models by imposing causally-driven information loss on biased features to learn confounders. Experimental results across various multimodal tasks, datasets, and backbones demonstrate the superiority of the proposed method.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "Y6YrbcTsuJ", "reviewer": "Reviewer_nFie", "summary": "", "questions": "Question:\n\n1. What’s the relationship between $C$ and $Q, A$? Are they independent?\n2. I suggest the author to include more related works [1, 2] and comparing baselines [3, 4] on causal-based VQA debiasing.\n3. The reference of VQA-CP in **Dataset** and Tab. 1 are **different**. Which dataset do you use? \n4. (Follow-up) In the baseline method **D-VQA**, experiments are conducted on VQA-CP v2. Why not compare on the same dataset?\n5. (Follow-up) Compared with the baseline method D-VQA (which is also trained with LXMERT), the proposed method (LXMERT+ATE-D or LXMERT+ATE-D) seems not achieve better performance. D-VQA_f +ATE-D still cannot outperform D-VQA. What are the advantages of the proposed method?\n6. (To sum up) I hope the author can experiment on existing benchmarking and compare with more recent highly-related works to demonstrate their effectiveness.\n7. In Tab. 1, why D-VQA_f + TE-D uses less #MFLOPS than D-VQA_f? I guess it needs two-stage training.\n8. I suggest the author to provide some straightforward visualizations on how the proposed method is able to learn confounder.\n\n[1] Niu, Yulei, et al. \"Counterfactual vqa: A cause-effect look at language bias.\" *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*. 2021.\n\n[2] Niu, Yulei, and Hanwang Zhang. \"Introspective distillation for robust question answering.\" *Advances in Neural Information Processing Systems* 34 (2021): 16292-16304.\n\n[3] Kolling, Camila, et al. \"Efficient counterfactual debiasing for visual question answering.\" *Proceedings of the IEEE/CVF winter conference on applications of computer vision*. 2022.\n\n[4] Cho, Jae Won, et al. \"Generative Bias for Robust Visual Question Answering.\" *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*. 2023.", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Response to Weaknesses:\n\n1. **Error in Fig. 1**: Thanks for pointing it out. We’ll replace the word “blue” in first line of the second image with a “red”.\n\nResponse to Questions:\n\n1. **What’s the relationship between C, Q and A?**: As we mention in Lines 210-215, the variable C in the causal graph is the confounder that creates a non-causal link between the multimodal variable M (e.g., multimodal features in models like LXMERT) and the answer A. The question variable Q in the VQA task causally affects the multimodal variable M. Thus, C also creates a non-causal path between Q and A via M and C.(Q→ M→C←A). C is the confounder.\n\n2. **Related works [1, 2] and baselines [3, 4] on causal-based VQA debiasing**: Thanks for suggesting the additional references. We implemented [3], [4] using a LXMERT model free of the data leakage issue we have explained in Lines 483-488 and the numbers are as follows:\n\nMethod | Overall | Yes/No | Number | Other |\n--------------------------------------------- | ------- | ----- | ---- | --- |\nLXMERT(Tan and Bansal, 2019) | 41.2 | 44.1 | 13.9 | 47.2 |\n+CD-VQA [3] (*new*) | 42.1 | 42.7 | 14.8 | 49.3 |\n+ATE-D (ours) | 42.2 | 43.6 | 14.6 | 49.0 |\n+TE-D (ours) | 43.4 | 48.3 | 14.4 | 48.8 |\n+GenB [4] (*new*)| **52.8** | **67.3** | **29.8** | 49.7 |\nD-VQA*f* | 43.9 | 47.2 | 15.9 | 49.9 |\nD-VQA | 52.4 | 65.5 | 29.7 | **51.8** |\n\nOur methods outperform the data augmentation-based approach proposed in [3]. Especially, TE-D outperforms [3] by 1.3% without creating augmented data for balancing out the bias. Results show that [4]**\\*** outperforms our methods as well as the previous SoTA i.e., D-VQA. [4] is an ensemble-based method and a multi-stage pipeline where they first train the (target) biased model, then train a separate generative model to model the bias and then use the generative model to debias the target model. Our work is different in that we model the bias from biased features using information theory and use it via causal methods to debias the target model in a single step. The goal of our work is not to achieve state-of-art results but to showcase the use of information minimization to learn confounders and the need for removing multimodal biases. We’ll add them to the Related Work section and the results in Table 1.\n\n**\\*** The numbers in [4] has the same data leakage issue that we talk about in Lines 483-488. So their LXMERT numbers are misleading and not comparable to our results. The numbers that we report above are after removing this issue which is why they don’t match those reported in their paper [4].\n\n3. **The reference of VQA-CP in Dataset and Tab. 1 are different. Which dataset do you use?**: Table 1 has Invariant VQA reference which we intended to put for the term IVQA-CP in the same line. The one in the dataset section is the actual  VQA-CP reference. We will correct it in the final version.\n\n4. **(Follow-up) In the baseline method D-VQA, experiments are conducted on VQA-CP v2. Why not compare on the same dataset?**: When we say VQA-CP, we mean VQA-CP v2. We do not use VQA-CP v1 anywhere. Our numbers are not comparable to the numbers reported in the D-VQA paper because it has the data leakage issue that we avoid in our work. See Lines  483-488. We have reproduced D-VQA in a setting free of data-leakage and report those numbers in Table 1.\n\n\n5. **(Follow-up) Compared with the baseline method D-VQA (which is also trained with LXMERT), the proposed method (LXMERT+ATE-D or LXMERT+ATE-D) seems not achieve better performance. D-VQA_f +ATE-D still cannot outperform D-VQA. What are the advantages of the proposed method?**: D-VQA explicitly models unimodal biases via unimodal branches, whereas we model biases using multimodal representations (See Figs 3, 4) without prior assumptions on what the biases look like which make it generalizable across modalities. Additionally, we demonstrate improvements over D-VQA_f with D-VQA_f + TE-D, showing that it is important to remove multimodal biases in addition to unimodal ones - which is one of the main goals of our work. D-VQA_f + ATE-D does not outperform D-VQA because D-VQA_f + ATE-D only performs feature-based debiasing whereas D-VQA is a combination of data augmentation and feature-based debiasing. We explicitly state that debiasing methods that use data augmentation are superior to debiasing methods that operate on features only [Lines 628-632]. Data augmentation is an expensive operation and needs prior knowledge about the kind of biases present in the dataset; hence, it is worth studying methods that perform feature-based debiasing only.\n\n6. **(To sum up) I hope the author can experiment on existing benchmarking and compare with more recent highly-related works to demonstrate their effectiveness.**: Thanks! We have added the baselines in the response to Q2.\n\n\n7. **In Tab. 1, why D-VQA_f + TE-D uses less #MFLOPS than D-VQA_f? I guess it needs two-stage training.**: We report the number of additional MFLOPS: D-VQA_f has 18.9 additional MFLOPS compared to LXMERT. D-VQA_f + TE-D has 8.8 additional MFLOPS compared to D-VQA_f. We will report the additional MFLOPS compared to LXMERT for D-VQA_f + TE-D to remove confusion in the updated version of our paper.\n\n\n8. **I suggest the author provide some straightforward visualizations on how the proposed method is able to learn confounder.**: We dedicate subsection Sec 7.2 to performing an analysis of whether the proposed methods actually learn the confounders that model biases in the dataset and show that the biases modeled by the learnt confounders indeed capture the dataset biases. See lines 530-554 and Fig 7. Additionally, we will expand Figures 3 and 4 to show step-by-step breakdown of the model and emphasize on the confounder learning portion of the pipeline to remove any confusion.\n\n[1] Niu, Yulei, et al. \"Counterfactual vqa: A cause-effect look at language bias.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021.\n\n[2] Niu, Yulei, and Hanwang Zhang. \"Introspective distillation for robust question answering.\" Advances in Neural Information Processing Systems 34 (2021): 16292-16304.\n\n[3] Kolling, Camila, et al. \"Efficient counterfactual debiasing for visual question answering.\" Proceedings of the IEEE/CVF winter conference on applications of computer vision. 2022.\n\n[4] Cho, Jae Won, et al. \"Generative Bias for Robust Visual Question Answering.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "Strength:\n\n1. This paper utilize causally-motivated information to learn confounder representations from biased features and utilize them to debias models. This approach offers a reasonable solution for making multi-modal models debiased.\n2. The paper is well-structured and mostly clear.", "reasons_to_reject": "Weakness:\n\n1. In Fig. 1, when cup removed, I think “LXMERT blue” should be wrong.\n2. My concerns mainly lie within the experimental section. See Questions.", "questions_to_authors": "Question:\n\n1. What’s the relationship between $C$ and $Q, A$? Are they independent?\n2. I suggest the author to include more related works [1, 2] and comparing baselines [3, 4] on causal-based VQA debiasing.\n3. The reference of VQA-CP in **Dataset** and Tab. 1 are **different**. Which dataset do you use? \n4. (Follow-up) In the baseline method **D-VQA**, experiments are conducted on VQA-CP v2. Why not compare on the same dataset?\n5. (Follow-up) Compared with the baseline method D-VQA (which is also trained with LXMERT), the proposed method (LXMERT+ATE-D or LXMERT+ATE-D) seems not achieve better performance. D-VQA_f +ATE-D still cannot outperform D-VQA. What are the advantages of the proposed method?\n6. (To sum up) I hope the author can experiment on existing benchmarking and compare with more recent highly-related works to demonstrate their effectiveness.\n7. In Tab. 1, why D-VQA_f + TE-D uses less #MFLOPS than D-VQA_f? I guess it needs two-stage training.\n8. I suggest the author to provide some straightforward visualizations on how the proposed method is able to learn confounder.\n\n[1] Niu, Yulei, et al. \"Counterfactual vqa: A cause-effect look at language bias.\" *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*. 2021.\n\n[2] Niu, Yulei, and Hanwang Zhang. \"Introspective distillation for robust question answering.\" *Advances in Neural Information Processing Systems* 34 (2021): 16292-16304.\n\n[3] Kolling, Camila, et al. \"Efficient counterfactual debiasing for visual question answering.\" *Proceedings of the IEEE/CVF winter conference on applications of computer vision*. 2022.\n\n[4] Cho, Jae Won, et al. \"Generative Bias for Robust Visual Question Answering.\" *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*. 2023.", "paper_topic_and_main_contributions": "In this paper, the authors explore bias arising from confounding factors within a causal graph for multimodal data. The authors investigate a novel approach that employs information minimization based on causal motivations to acquire representations of these confounding factors and employ methods rooted in causal theory to mitigate bias within models. Our findings indicate that the acquired confounder representations effectively capture dataset biases, and the proposed debiasing techniques enhance the out-of-distribution (OOD) performance across multiple multimodal datasets, all while maintaining in-distribution performance. Moreover, The authors also identify the efficiency of spurious features in models' predictions, providing further evidence of the efficacy of our suggested approaches.\n", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "rkM0mnKjRX", "reviewer": "Reviewer_JDbk", "summary": "", "questions": "", "limitations": "", "rating": 2, "confidence": 2, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "1. **The line 262-266 is not easy to understand why there is a causal relationship? Why can the latent representation approximate possible observations?**: Confounders are variables that affect both the input and the outcome without creating a causal link between them (see path M->C->A in Fig 3). These undesirable correlations are caused by biases in the dataset which create a non-causal link between input and outcome (for example, the answer to most questions starting with “What sport” is “tennis” in the dataset. But “What sport” and “tennis” are not causally correlated). Past work [2] has shown that deep models preferentially encode dataset shortcuts under limited representation capacity and confounders can be modeled as low-dimensional vectors [Lines 179-182, 295-299]. We use the same principle in our proposed methods to learn confounders (or dataset biases) by imposing information loss on multimodal features. In ATE-D, the latent representations of an autoencoder are a result of compressing the original representations into low-dimensional vectors and are thereby expected to capture biases that represent the confounders [Lines 313-316, also see citations in Line 266]. Intuitively, confounders are biases which have low variance and high bias and feature compression helps capture biases.\n\n\n\n2. **It would be great to show some cases in explaining average treatment effect and total effect.**: Since these are well-known theories in causality, it will be difficult to devote the limited space in the main text towards showcasing their examples, however, we will include some examples in Appendix and point the readers towards other appropriate resources on the internet like [1].\n\n\n3. **Line 282 is not clear or straightforward enough. In this paper, what bias is considered and why models over-rely on spurious features? Higher co-occurrence between some features and labels?**: Yes, spurious features are the undesirable features that models tend to rely on because of higher correlations between certain features and the label in the dataset. The models tend to learn a causal relationship between the features and the label when actually it is not causal but caused by confounding variables that represent the spurious correlation. See Lines 66-72. Also see paper cited in line 282. \n\n4. **In the step 1 of section 4.1, why can leveraging the latent representation of a small autoencoder help to get the features related to spurious correlation? Intuitively smaller models are easy to learn spurious correlations but omit non-spurious correlations. However can we evaluate to what extent spurious correlation-related features have been extracted?**: The latent representations are obtained by compressing the original features which means they capture the most common biases in the dataset which model the confounders. See a detailed explanation in our response to Question 1. We dedicate an entire subsection Section 7.2 to perform an analysis of whether the proposed methods actually learn the confounders that model biases in the dataset and show that the biases modeled by the learnt confounders indeed capture the dataset biases. See lines 530-554 and Figure 7.\n\n\n5. **What is the format of $g_j$? Why equation (3) has to be explained through backdoor adjustment?**: It is an instantiation of backdoor adjustment which is commonly used for causal debiasing [1]. See Lines 243-266 in Section 3.\n\n6. **The core assumption of the spurious feature extraction step in TE-D is spurious features should be of higher variance. Why?**: We assume that the reviewer meant lower variance, as mentioned in Lines 361-365. Spurious features are the biases present in the dataset that can be easily learned by a model with low modeling capacity (e.g., shallow neural network models) to ‘solve’ the dataset. These biases stem from high co-occurrence between inputs and outputs present in the dataset and represent simple explanations for why an input has a particular output. For example, in the VQA-CP dataset, most questions starting with ‘Is there’ have an answer ‘yes’. Thus, the features learnt by a shallow model will learn to only encode ‘Is the’ and ignore all of the following words in the question, resulting in low-information content and low variance.\n\n\n7. **What's the motivation behind ATE-D and TE-D in terms of average treatment effect and total effect respectively?**: Both are debiasing methods that use the respective causal debiasing theories. See lines 243-280. We are instantiating the respective causal theories to debias with the help of confounders obtained by leveraging information minimization.\n\n\n8. **The related work about “Inductive Bias in Model Architecture” is not clear and does not show what typical inductive bias is like when it comes to the architect of a vision-language model. It would be great if the related work section can firstly show the full-picture of each related technique and then a brief description of each key work.**: Thank you for the suggestion, we will follow your suggestion and reformat the Related Work with headers like this:\n\n- Unimodal inductive bias:  [3, 4, 5] studies utilize a separate QA branch in their models.  By doing so, the models become less dependent on language priors and are forced to focus more on visual information.\n\n- Confounder-modeling inductive bias:  Inductive bias of limiting the representational capacity of the model for encoding confounders in the data [6, 7].\n\nWe will similarly reformat other parts of the related works section.\n\n\n\n\n\n[1] [Introduction to Causal Theory slides](https://www.bradyneal.com/slides/4%20-%20Causal%20Models.pdf)\n\n[2] Yang, Wanqian, et al. \"Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers.\" Advances in Neural Information Processing Systems 35 (2022): 20351-20365.\n\n[3] Clark, Christopher, Mark Yatskar, and Luke Zettlemoyer. \"Don’t Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases.\" Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.\n\n[4] Cadene, Remi, et al. \"Rubi: Reducing unimodal biases for visual question answering.\" Advances in neural information processing systems 32 (2019).\n\n[5] Ramakrishnan, Sainandan, Aishwarya Agrawal, and Stefan Lee. \"Overcoming language priors in visual question answering with adversarial regularization.\" Advances in Neural Information Processing Systems 31 (2018).\n\n[6] Kallus, Nathan, Xiaojie Mao, and Madeleine Udell. \"Causal inference with noisy and missing covariates via matrix factorization.\" Advances in neural information processing systems 31 (2018).\n\n[7] Sen, Rajat, et al. \"Contextual bandits with latent confounders: An nmf approach.\" Artificial Intelligence and Statistics. PMLR, 2017.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. Two solutions from different angles.\n2. Good visual cases to describe the content of this paper.", "reasons_to_reject": "1. The line 262-266 is not easy to understand why there is a causal relationship? Why can the latent representation approximate possible observations?\n2. It would be great to show some cases in explaining average treatment effect and total effect.\n3. Line 282 is not clear or straightforward enough. In this paper, what bias is considered and why models over-rely on spurious features? Higher co-occurrence between some features and labels? \n4. In the step 1 of section 4.1, why can leveraging the latent representation of a small autoencoder help to get the features related to spurious correlation? Intuitively smaller models are easy to learn spurious correlations but omit non-spurious correlations. However can we evaluate to what extent spurious correlation-related features have been extracted?\n5. What is the format of g_j? Why equation (3)  has to be explained through backdoor adjustment?\n6. The core assumption of the spurious feature extraction step in TE-D is spurious features should be of higher variance. Why?\n7. What's the motivation behind ATE-D and TE-D in terms of average treatment effect and total effect respectively?\n8. The related work about “Inductive Bias in Model Architecture” is not clear and does not show what typical inductive bias is like when it comes to the architect of a vision-language model. It would be great if the related work section can firstly show the full-picture of each related technique and then a brief description of each key work.", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper proposes a debiasing method for vision-language models in a multi-modality setting. The authors proposed two different methods for extracting features that are related to spurious correlation in the training data.", "excitement": 2, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper presents an approach for debiasing VQA models by identifying features that are likely to have spurious correlations with labels---those that are have low information content but are still predictive of the labels---using methods motivated by causal theory (Average Treatment Effect and Rate Distortion), and mitigate those correlations by feature reweighting and additional losses. Experiments compare the proposed approaches with models that do not use any debiasing or use debiasing techniques leveraging knowledge of biases, including unimodal feature based debiasing and data augmentation. The proposed approaches do better than all these except those that use data augmentation, but the authors argue that data augmentation can often be expensive and requires knowledge of biases.

The reviewers recommended providing additional explanations and visualizations to further clarify how the methods work and the authors have agreed to do so.

In addition, I would encourage the authors to include
1. a discussion on the limitations of targeted data augmentation methods for debiasing models, and in what scenarios _discovering_ spurious correlations would be practically more useful. It might help to include experiments on a wider range of distribution shifts to show the benefits of the proposed techniques over data augmentation. This would be helpful particularly given the large gap in performance between D-VQA and the proposed method.
2. a discussion on how the proposed approach relates to work on slice discovery methods (e.g.: Eyuboglu et al., 2022).


Reference

Eyuboglu et al., 2022: Domino: Discovering Systematic Errors with Cross-Modal Embeddings, ICLR 2022
Decision: Accept-Findings