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Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers
Divyat Mahajan, Chenhao Tan, Amit Sharma
To construct interpretable explanations that are consistent with the original ML model, counterfactual examples---showing how the model's output changes with small perturbations to…
Quantifying Infra-Marginality and Its Trade-off with Group Fairness
Arpita Biswas, Siddharth Barman, Amit Deshpande +1
In critical decision-making scenarios, optimizing accuracy can lead to a biased classifier, hence past work recommends enforcing group-based fairness metrics in addition to maximiz…
Alleviating Privacy Attacks via Causal Learning
Shruti Tople, Amit Sharma, Aditya Nori
Machine learning models, especially deep neural networks have been shown to be susceptible to privacy attacks such as membership inference where an adversary can detect whether a d…
Quantifying Error in the Presence of Confounders for Causal Inference
Rathin Desai, Amit Sharma
Estimating average causal effect (ACE) is useful whenever we want to know the effect of an intervention on a given outcome. In the absence of a randomized experiment, many methods…
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
Ramaravind Kommiya Mothilal, Amit Sharma, Chenhao Tan
Post-hoc explanations of machine learning models are crucial for people to understand and act on algorithmic predictions. An intriguing class of explanations is through counterfact…
Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data
Jackson A. Killian, Bryan Wilder, Amit Sharma +4
Digital Adherence Technologies (DATs) are an increasingly popular method for verifying patient adherence to many medications. We analyze data from one city served by 99DOTS, a phon…