Center for Causal Inference Symposium
Thursday, June 24, 2021
10:00 a.m. - 5:00 p.m. EDT/7:00 a.m. - 2 p.m. PDT
This event is free and will take place on Zoom.
At the heart of policy analysis is cause and effect: How can we determine or quantify the actual impact of an action or policy? Without proper methods, recklessly inferring cause and effect from observed relationships can inform unhelpful policies at best, and dangerous ones at worst. More than ever, the research community needs the development and understanding of causal inference methods.
The 2021 RAND Center for Causal Inference (CCI) Inaugural Symposium, hosted by the RAND CCI, is a one-day interdisciplinary virtual symposium that will offer researchers the opportunity to present and learn about cutting-edge causal inference research in statistics, econometrics, and other quantitative fields.
How to Participate
Individuals interested in attending the event can register online.
All times below are Eastern U.S. Presenting authors are listed in italics.
- 10:00 a.m.
- Opening remarks
- 10:05 a.m.
- Varying impacts of letters of recommendation on college admissions: Approximate balancing weights for subgroup effects in observational studies
- Eli Ben-Michael, Avi Feller, and Jesse Rothstein
- 10:40 a.m.
- Broken Instruments
- Trevor Gallen and Benjamin Raymond
- 11:15 a.m.
- 11:25 a.m.
- Targeting for Long-Term Outcomes
- Jeremy Yang, Dean Eckles, Paramveer Dhillon, and Sinan Aral
- 12:00 p.m.
- A Method for Increasing Statistical Power in the Evaluation of Dilute Interventions
- John L. Adams, Anna C. Davis, and Michaela M. Hull
- 12:35 p.m.
- Lunch Break
- 1:35 p.m.
- RAND and the Center for Causal Inference
- 1:45 p.m.
- Ananke: A Python Package For Causal Inference With Graphical Models
- Rohit Bhattacharya, Jaron J. R. Lee, and Razieh Nabi
- 2:20 p.m.
- Experimental Evaluation of Algorithm-Assisted Human Decision-Making: Application to Pretrial Public Safety Assessment
- Kosuke Imai, Zhichao Jiang, James Greiner, Ryan Halen, and Sooahn Shin
- 2:55 p.m.
- 3:05 p.m.
- Double Machine Learning-based Program Evaluation under Unconfoundedness
- 3:40 p.m.
- Non-Random Exposure to Exogenous Shocks: Theory and Applications
- Kirill Borusyak and Peter Hull
- 4:15 p.m.
- 4:20 p.m.
- Minimax-Optimal Policy Learning Under Unobserved Confounding
- Nathan Kallus and Angela Zhou
- 4:55 p.m.
- Dynamic Covariate Balancing: Estimating Treatment Effects over Time
- Davide Viviano and Jelena Bradic
- 5:30 p.m.
John L. Adams is a professor of Health Systems Science at Kaiser Permanente Bernard J. Tyson School of Medicine.
Matthew Baird is codirector of the RAND Center for Causal Inference.
Eli Ben-Michael is a postdoctoral fellow at the Institute for Quantitative Social Science at Harvard University.
Rohit Bhattacharya is an incoming assistant professor of computer science at Williams College.
Trevor Gallen is an assistant professor of economics at Purdue University.
Peter Hull is an assistant professor in economics at the University of Chicago.
Kosuke Imai is a professor in the Department of Government and the Department of Statistics at Harvard University.
Michael Knaus is an assistant professor of econometrics at the Swiss Institute for Empirical Economic Research of the University of St. Gallen.
Layla Parast is codirector of the RAND Center for Causal Inference.
Davide Viviano is a Ph.D. student in economics at the University of California - San Diego.
Jeremy Yang is a Ph.D. candidate at the MIT Sloan School of Management.
Angela Zhou is a research fellow at the Simons Institute at UC Berkeley and incoming assistant professor at USC Marshall Data Sciences and Operations.