Algorithmic Aspects of Causal Inference

Program

Causality

Date

Monday, Mar. 21 – Thursday, Mar. 24, 2022

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All talks are listed in Pacific Time.

9 – 9:25 a.m.

Coffee & Check In

9:25 – 9:30 a.m.

Opening Remarks

9:30 – 10:10 a.m.

Tractable Probabilistic Circuits

Guy Van den Broeck (UCLA)

10:10 – 10:40 a.m.

Break

10:40 – 11:20 a.m.

Neural Networks And Spurious Correlations

Aditi Raghunathan (UC Berkeley)

11:20 a.m. – 12 p.m.

Learning Restricted Boltzman Machines

Ankur Moitra (MIT)

12 – 2 p.m.

Lunch

2 – 2:30 p.m.

Learning Ill-Conditioned Gaussian Graphical Models

Raghu Meka (UCLA)

2:30 – 3 p.m.

Preconditioning In Sparse Linear Regression Using Graphical Structure

Frederic Koehler (Stanford)

3 – 3:30 p.m.

Break

3:30 – 4 p.m.

Causal Matrix Completion

Anish Agarwal (UC Berkeley)

4 – 4:30 p.m.

Parameter Estimation For Undirected Graphical Models With Hard Constraints

Kavita Ramanan (Brown University)

4:30 – 5:30 p.m.

Reception

9 – 9:30 a.m.

Coffee & Check In

9:30 – 10:10 a.m.

Efficient Distance Estimation And Causal Inference For Discrete Models

Arnab Bhattacharyya (National University of Singapore)

10:10 – 10:40 a.m.

Break

10:40 – 11:20 a.m.

Identifying Mixture Models

Leonard J. Schulman (Caltech)

11:20 a.m. – 12 p.m.

Identifying Mixtures Of Bayesian Network Distributions

Yuval Rabani (The Hebrew University of Jerusalem)

12 – 2 p.m.

Lunch

2 – 2:30 p.m.

Asymptotically Best Causal Effect Identification With Multi-Armed Bandits

Silvia Chiappa (DeepMind)

2:30 – 3 p.m.

Confounding-Robust Policy Evaluation In Infinite-Horizon Reinforcement Learning

Angela Zhou (Berkeley)

3 – 3:30 p.m.

Break

3:30 – 4 p.m.

Stability Of Causal Identification From The Perspective Of Condition Numbers

Piyush Srivastava (Tata Institute of Fundamental Research)

4 – 4:30 p.m.

Causalsim: Trace-Driven Simulation For Network Protocols

Devavrat Shah (MIT)

4:30 – 5:30 p.m.

Open Problem Session (In-Person Only)

9 – 9:30 a.m.

Coffee & Check In

9:30 – 10:10 a.m.

A Multi-Group Approach To Algorithmic Fairness

Guy Rothblum (Weizmann Institute of Science)

10:10 – 10:40 a.m.

Break

10:40 – 11:20 a.m.

Decision-Making Under Miscalibration

Gal Yona (Weizmann Institute)

11:20 a.m. – 12 p.m.

Multicalibration, Universal Adaptability and Causality

Omer Reingold (Stanford University)

12 – 1:40 p.m.

Lunch

1:40 – 2:20 p.m.

Algorithmic Fairness From The Lens Of Causality And Information Theory

Sanghamitra Dutta (JP Morgan AI Research)

2:20 – 3 p.m.

Orthogonal Statistical Learning

Vasilis Syrgkanis (Microsoft Research)

3:30 – 5 p.m.

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)

9 – 9:30 a.m.

Coffee & Check In

9:30 – 10:10 a.m.

The Approximate Implication Problem For Probabilistic Graphical Models

Batya Kenig (Technion, Israel Institute of Technology)

10:10 – 10:40 a.m.

Break

10:40 – 11:20 a.m.

Challenges For Causal Inference On Digital Platforms

Moritz Hardt (Max Planck Institute for Intelligent Systems)

11:20 a.m. – 12 p.m.

Active Invariant Causal Prediction: Experiment Selection Through Stability

Christina Heinze-Deml (Apple Health AI)

12 – 2 p.m.

Lunch

2 – 2:30 p.m.

Graph Agnostic Randomized Experimental Design under Heterogeneous Linear Network Interference

Christina Yu (Cornell University)

2:30 – 3 p.m.

Collaborative Causal Discovery With Atomic Interventions

Shiva Kasiviswanathan (Amazon)

3 – 3:30 p.m.

Break

3:30 – 4 p.m.

New Approaches To Learning Nonparametric (Latent) CausalGraphical Models

Bryon Aragam (University of Chicago)

4 – 4:30 p.m.

Kernel Methods For Causal Inference

Rahul Singh (MIT)