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I am an associate professor in the Department of Computer Science and the director of the Causal Artificial Intelligence Lab at Columbia University. Prior to joining Columbia, I was an assistant professor at Purdue University. Before that, I obtained my Ph.D. in Computer Science at the University of California, Los Angeles, advised by Judea Pearl. I am broadly interested in Artificial Intelligence, Machine Learning, Statistics, Robotics, Cognitive Science, and Philosophy of Science.

My research focuses on causal inference and its applications to data-driven fields (i.e., data science) in the health and social sciences as well as artificial intelligence and machine learning. I am particularly interested in understanding how to make robust and generalizable causal and counterfactual claims in the context of heterogeneous and biased data collections, including due to issues of confounding bias, selection bias, and external validity (transportability). A survey of recent developments on this topic, when combining massive sets of research data, appeared at the Proceedings of the National Academy of Sciences (PNAS), see the story and the paper. A brief summary of the automated scientist project was also highlighted at the IEEE Intelligent Systems (link, story). For an overview of my thoughts on causal data science (as of April 2024), watch the talk I recently gave at Columbia University, link. For some of the latest results on the topic, see: (小火箭付费服务器节点, ICML-19, AAAI-20, AAAI-20, 小火箭付费节点购买).

More recently, I have been exploring the intersection of causal inference with decision-making/reinforcement learning (NeurIPS-15, ICML-17, IJCAI-17, NeurIPS-18, AAAI-19, NeurIPS-19, 小火箭ssr永久免费节点) and explainability/fairness analysis (AAAI-18, NeurIPS-18, UAI-19).

Additional information (Jul/1, 2024) -- CV (pdf), short bio (txt), hi-res picture (jpg).

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Our chapter "On Pearl’s Hierarchy and the Foundations of Causal Inference" (with Juan Correa, Duligur Ibeling, Thomas Icard) will appear at an ACM special volume in honor of Judea Pearl and is now available online (小火箭免费节点).
The slides and videos of my tutorial at ICML-20 on the intersection of causal inference and reinforcement learning, which I have been calling "causal reinforcement learning" (CRL), are now available online (link).
The video of my talk at Columbia University on "causal data science" -- the intersection of causal inference and data science -- is now available online (link).
Our paper "General Identifiability with Arbitrary Surrogate Experiments" (with Sanghack Lee and Juan Correa, pdf) was selected as the Best Paper Award (1 out 450 papers) at the Uncertainty in Artificial Intelligence conference (UAI-19).
I am joining the Computer Science Department at Columbia University.
I am co-organizing with J. Pearl, B. Scholkopf, C. Szepesvari, S. Mahadevan, P. Tadepalli the AAAI-19 Spring Symposium "Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-based AI" (WHY19), consider submitting your work (link).
I am thankful for Adobe's generous gift ($50k) and support to our research.
Our paper "Causal Identification under Markov Equivalence" (with Amin Jaber and Jiji Zhang, link) was selected as the Best Student Paper Award (1 out 337 papers) at the Uncertainty in Artificial Intelligence conference (UAI-18).
Our paper "Generalized Adjustment Under Confounding and Selection Biases" (with Juan Correa and Jin Tian, link) just received the Outstanding Paper Award Honorable Mention (2 out 3800 papers) at the Annual Conference of the American Association for Artificial Intelligence (AAAI-18).
I am thankful for IBM's generous gift ($50k) and support to our research and collaboration.
I am joining the Editorial Board of the Journal of Causal Inference (link), consider submitting your work.
I am co-organizing the 7th UAI Causality Workshop: Learning, Inference, and Decision-Making (link), consider submitting your work.
Our work on solving big data's fusion problem and combining massive sets of research data just appeared at the Proceedings of the National Academy of Sciences (PNAS), see story and paper.
I am honored to be selected by IEEE Intelligent Systems as one of AI's 10 To Watch (story, pdf).
I am co-organizing the 2016 ACM SIGKDD Workshop on Causal Discovery (小火箭付费节点购买) and the 2016 UAI Workshop on Causation: Foundation to Application (小火箭免费节点公众号), consider submitting your work.
Our paper "Recovering from selection bias in causal and statistical inference" was selected as a notable paper in computing in 2014, to appear in the ACM Computing Reviews' 19th Annual Best of Computing (see full list here).
I will join the Computer Science Department at Purdue as an Assistant Professor in the Fall/2015.
I was selected as the 2014 Edward K. Rice Outstanding Doctoral Student. This award is given to a single PhD student in all engineering and applied sciences majors at UCLA.
Our paper "Recovering from Selection Bias in Causal and Statistical Inference" (link) just received the best paper award (1 out 1406 papers) at the Annual Conference of the American Association for Artificial Intelligence (AAAI-14).
I am honored that I was selected as the "Outstanding Graduating PhD Student" (commencement award), Computer Science, UCLA.
I received the "Google Outstanding Graduate Research Award", Computer Science, UCLA.
I am honored to be selected as one of the 2014 Dan David Scholars for "outstanding achievement and future promise" in the field of Artificial Intelligence (citation here).
I am co-organizing an ICML-14 workshop on Causal Modeling & Machine Learning (with B. Scholkopf, K. Zhang, JJ. Zhang), consider submitting your work, link.
I am a guest editor (with J. Pearl, B. Scholkopf, K. Zhang, J. Li) of ACM Transactions on Intelligent Systems and Technology on "Causal Discovery and Inference". See the call for papers.
With Judea Pearl, I gave a tutorial on "Causes and Counterfactuals: Concepts, Principles and Tools" at NeurIPS 2013. The video (with slides) is available online, link (requires HTML5).
The video of my talk on meta-transportability in AISTATS-2013 is now available here.