19 citations · 53 across the 13 of their papers we have counts for
13 papers
Controlling Learned Effects to Reduce Spurious Correlations in Text Classifiers
Parikshit Bansal, Amit Sharma
To address the problem of NLP classifiers learning spurious correlations between training features and target labels, a common approach is to make the model's predictions invariant…
On Counterfactual Data Augmentation Under Confounding
Abbavaram Gowtham Reddy, Saketh Bachu, Saloni Dash +3
Counterfactual data augmentation has recently emerged as a method to mitigate confounding biases in the training data. These biases, such as spurious correlations, arise due to var…
Counterfactual Generation Under Confounding
Abbavaram Gowtham Reddy, Saloni Dash, Amit Sharma +1
A machine learning model, under the influence of observed or unobserved confounders in the training data, can learn spurious correlations and fail to generalize when deployed. For…
The Counterfactual-Shapley Value: Attributing Change in System Metrics
Amit Sharma, Hua Li, Jian Jiao
Given an unexpected change in the output metric of a large-scale system, it is important to answer why the change occurred: which inputs caused the change in metric? A key componen…
Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization
Jivat Neet Kaur, Emre Kiciman, Amit Sharma
Recent empirical studies on domain generalization (DG) have shown that DG algorithms that perform well on some distribution shifts fail on others, and no state-of-the-art DG algori…
Long Story Short: Omitted Variable Bias in Causal Machine Learning
Victor Chernozhukov, Carlos Cinelli, Whitney Newey +2
We develop a general theory of omitted variable bias for a wide range of common causal parameters, including (but not limited to) averages of potential outcomes, average treatment…
Matching Learned Causal Effects of Neural Networks with Domain Priors
Sai Srinivas Kancheti, Abbavaram Gowtham Reddy, Vineeth N Balasubramanian +1
A trained neural network can be interpreted as a structural causal model (SCM) that provides the effect of changing input variables on the model's output. However, if training data…
The Connection between Out-of-Distribution Generalization and Privacy of ML Models
Divyat Mahajan, Shruti Tople, Amit Sharma
With the goal of generalizing to out-of-distribution (OOD) data, recent domain generalization methods aim to learn "stable" feature representations whose effect on the output remai…
DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions
Amit Sharma, Vasilis Syrgkanis, Cheng Zhang +1
Estimation of causal effects involves crucial assumptions about the data-generating process, such as directionality of effect, presence of instrumental variables or mediators, and…
Causally Constrained Data Synthesis for Private Data Release
Varun Chandrasekaran, Darren Edge, Somesh Jha +3
Making evidence based decisions requires data. However for real-world applications, the privacy of data is critical. Using synthetic data which reflects certain statistical propert…
Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions
Yanbo Xu, Divyat Mahajan, Liz Manrao +2
For many kinds of interventions, such as a new advertisement, marketing intervention, or feature recommendation, it is important to target a specific subset of people for maximizin…
DoWhy: An End-to-End Library for Causal Inference
Amit Sharma, Emre Kiciman
In addition to efficient statistical estimators of a treatment's effect, successful application of causal inference requires specifying assumptions about the mechanisms underlying…
Evaluating and Mitigating Bias in Image Classifiers: A Causal Perspective Using Counterfactuals
Saloni Dash, Vineeth N Balasubramanian, Amit Sharma
Counterfactual examples for an input -- perturbations that change specific features but not others -- have been shown to be useful for evaluating bias of machine learning models, e…