Amit Sharma
Principal Researcher | Microsoft Research India

Data tells stories. My research aims to tell the causal story.
As machine learning systems move into societally critical domains such as healthcare, education, finance and criminal justice, questions on their impact gain fundamental importance. The key insight in my work is to consider modern algorithms as interventions, just like a medical treatment or an economic policy. Unlike typical interventions studied in social and biomedical sciences, however, algorithmic interventions can be arbitrarily complex. I work on developing methods to estimate causal impact of such systems and build algorithms that optimize the causal effect. I am also passionate about designing new interventions for societal impact, especially in healthcare.
If you are interested in working with me at MSR India, drop me an email. We hire interns throughout the year. There are also postdoctoral positions available. Additionally, if you are an undergraduate or a masters student, our lab runs an excellent pre-doctoral Research Fellows. program.
-
[2015] Ph.D. in Computer Science, Cornell University
[2010] B.Tech. in Computer Science, IIT Kharagpur
-
Causal inference | Causality and machine learning
AI reasoning | Accelerating scientific discovery
news
Jul 25, 2022 | Talk on necessity of causal inference for out-of-distribution generalization in prediction and decision-making at the Technion, Israel. [Slides] |
---|---|
May 31, 2022 | DoWhy library for causal inference evolves to an independent py-why org to foster wider collaboration. Contributions welcome! [Blog][Github][Arxiv] |
Dec 02, 2021 | Talk on Causal Inference for Machine Learning: Generalization, Explanation and Fairness; at the UK Office for National Statistics. [Slides] |
Dec 03, 2020 | Emre and I gave a Microsoft Research webinar on causal inference and its implications for machine learning. [Video] |
Aug 13, 2020 | Featured on the Humans of AI podcast. [Apple Podcasts][Spotify] |
Jul 23, 2020 | Session on causal machine learning with Elias Bareinboim, Susan Athey, and Cheng Zhang. [Video] |
May 30, 2019 | DiCE: Using counterfactual examples to explain machine learning. [Paper][Python Library][Blog] |
Aug 19, 2018 | Emre and I gave a tutorial on causal inference at KDD. [Slides] |
latest posts
selected publications
- TMLR 2024Causal reasoning and large language models: Opening a new frontier for causalityTransactions on Machine Learning Research, Aug 2024
- FAccT 2020Explaining machine learning classifiers through diverse counterfactual explanationsProceedings of the 2020 ACM conference on Fairness, Accountability and Transparency (FAccT), Aug 2020
- SciencePrediction and explanation in social systemsIn Science, Aug 2017