At this seminar you will hear from Amit Sharma who  is a Principal Researcher at Microsoft Research India. His work bridges causal inference techniques with data mining and machine learning, with the goal of making machine learning models generalize better, be explainable and avoid hidden biases.

Abstract
As predictive models based on machine learning (ML) are applied in diverse domains from healthcare to finance, reliability of the black-box models is one of the biggest problems facing ML research today: would the model generalize to out-of-distribution data, would it be fair to different subpopulations, and would it be explainable?

Amit will show how incorporating causality in predictive models can be useful for all these problems. Standard causal inference methods like matching can be applied to make deep learning models generalize to unseen distributions. A simple method based on matching obtains state-of-the-art results on domain generalization benchmarks. Similarly, the concept of counterfactuals is essential to explain machine learning models. Amit will describe work on automatically finding counterfactuals for any prediction and using it to create counterfactual-based explanations.

Finally, Amit will show that causal graphs are fundamental to understand the fairness of a ML system. Common fairness metrics fail to ensure fairness unless they account for the causal process that generates a given dataset.