Workshops

Spring 2022

Algorithmic Aspects of Causal Inference

Mar 21, 2022 to Mar 24, 2022

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All talks are listed in Pacific Time.

Monday, March 21st, 2022
9:00 am–9:25 am		Coffee & Check In
9:25 am–9:30 am		Opening Remarks
9:30 am–10:10 am		Tractable Probabilistic Circuits Guy Van den Broeck (UCLA)
10:10 am–10:40 am		Break
10:40 am–11:20 am		Neural Networks And Spurious Correlations Aditi Raghunathan (UC Berkeley)
11:20 am–12:00 pm		Learning Restricted Boltzman Machines Ankur Moitra (MIT)
12:00 pm–2:00 pm		Lunch
2:00 pm–2:30 pm		Learning Ill-Conditioned Gaussian Graphical Models Raghu Meka (UCLA)
2:30 pm–3:00 pm		Preconditioning In Sparse Linear Regression Using Graphical Structure Frederic Koehler (Stanford)
3:00 pm–3:30 pm		Break
3:30 pm–4:00 pm		Causal Matrix Completion Anish Agarwal (UC Berkeley)
4:00 pm–4:30 pm		Parameter Estimation For Undirected Graphical Models With Hard Constraints Kavita Ramanan (Brown University)
4:30 pm–5:30 pm		Reception

Tuesday, March 22nd, 2022
9:00 am–9:30 am		Coffee & Check In
9:30 am–10:10 am		Efficient Distance Estimation And Causal Inference For Discrete Models Arnab Bhattacharyya (National University of Singapore)
10:10 am–10:40 am		Break
10:40 am–11:20 am		Identifying Mixture Models Leonard J. Schulman (Caltech)
11:20 am–12:00 pm		Identifying Mixtures Of Bayesian Network Distributions Yuval Rabani (The Hebrew University of Jerusalem)
12:00 pm–2:00 pm		Lunch
2:00 pm–2:30 pm		Asymptotically Best Causal Effect Identification With Multi-Armed Bandits Silvia Chiappa (DeepMind)
2:30 pm–3:00 pm		Confounding-Robust Policy Evaluation In Infinite-Horizon Reinforcement Learning Angela Zhou (Berkeley)
3:00 pm–3:30 pm		Break
3:30 pm–4:00 pm		Stability Of Causal Identification From The Perspective Of Condition Numbers Piyush Srivastava (Tata Institute of Fundamental Research)
4:00 pm–4:30 pm		Causalsim: Trace-Driven Simulation For Network Protocols Devavrat Shah (MIT)
4:30 pm–5:30 pm		Open Problem Session (In-Person Only)

Wednesday, March 23rd, 2022
9:00 am–9:30 am		Coffee & Check In
9:30 am–10:10 am		A Multi-Group Approach To Algorithmic Fairness Guy Rothblum (Weizmann Institute of Science)
10:10 am–10:40 am		Break
10:40 am–11:20 am		Decision-Making Under Miscalibration Gal Yona (Weizmann Institute)
11:20 am–12:00 pm		Multicalibration, Universal Adaptability and Causality Omer Reingold (Stanford University)
12:00 pm–1:40 pm		Lunch
1:40 pm–2:20 pm		Algorithmic Fairness From The Lens Of Causality And Information Theory Sanghamitra Dutta (JP Morgan AI Research)
2:20 pm–3:00 pm		Orthogonal Statistical Learning Vasilis Syrgkanis (Microsoft Research)
3:30 pm–5:00 pm		Theoretically Speaking — Opportunities for the Application of Quantitative Models in a Fully Integrated Healthcare System Noa Dagan (Harvard, Clalit Research Institute and Ben-Gurion University) Noam Barda (Harvard, Tel-HaShomer Medical Center and Ben-Gurion University)

Thursday, March 24th, 2022
9:00 am–9:30 am		Coffee & Check In
9:30 am–10:10 am		The Approximate Implication Problem For Probabilistic Graphical Models Batya Kenig (Technion, Israel Institute of Technology)
10:10 am–10:40 am		Break
10:40 am–11:20 am		Challenges For Causal Inference On Digital Platforms Moritz Hardt (Max Planck Institute for Intelligent Systems)
11:20 am–12:00 pm		Active Invariant Causal Prediction: Experiment Selection Through Stability Christina Heinze-Deml (Apple Health AI)
12:00 pm–2:00 pm		Lunch
2:00 pm–2:30 pm		Graph Agnostic Randomized Experimental Design under Heterogeneous Linear Network Interference Christina Yu (Cornell University)
2:30 pm–3:00 pm		Collaborative Causal Discovery With Atomic Interventions Shiva Kasiviswanathan (Amazon)
3:00 pm–3:30 pm		Break
3:30 pm–4:00 pm		New Approaches To Learning Nonparametric (Latent) CausalGraphical Models Bryon Aragam (University of Chicago)
4:00 pm–4:30 pm		Kernel Methods For Causal Inference Rahul Singh (MIT)