Causal Inference Seminars and Training

One of the key roles of the Center for Causal Inference is to inform and educate fellow RAND researchers about causal inference methods. We do this via training sessions and videos, which we make available internally, and regular "brownbag seminars."

Causal Inference Symposia

Center for Causal Inference Holds Second Annual Symposium
The second annual RAND Center for Causal Inference (CCI) Symposium featured presentations by 16 researchers on cutting-edge causal inference research in statistics, econometrics, and other quantitative fields, including such topics as quasi-experimental methods, CI tools and applications, and balance and weighting .
Aug 18, 2022
Center for Causal Inference Inaugural Symposium
The 2021 RAND Center for Causal Inference (CCI) Annual Symposium was a one-day interdisciplinary virtual symposium that offered researchers the opportunity to present and learn about cutting-edge causal inference research in statistics, econometrics, and other quantitative fields.
Jun 24, 2021

Brownbag Seminars

2021

Separating the “Causal” from the “Machine Learning:” Improved Estimation of Treatment Effects through Residualization: Isaac Opper
Optimally Balanced Weighting Estimators for Causal Inference: Brian Vegetabile
Discussion of Recent Updates in the Difference-in-Differences Literature: Christine Mulhern
Evaluating Behavioral and Policy Responses to the COVID-19 Pandemic: Chris Whaley
Functional Synthetic Controls: Denis Agniel
Willingness-to-Pay for School Quality - An Application of Spatial First Differences: Jason Ward
Estimating Treatment Utilization and Supply Measures for Substance Abuse Treatment Facilities: Maria DeYoreo and Jonathan Cantor

2020

Using Machine Learning Techniques to Improve Average Treatment Effect Estimates in Small-Scale Randomized Controlled Trials: Isaac Opper
Brainstorming session on identifying peer effects using similarity matrices, applied to diet and neighborhood satisfaction: Matt Baird, RAND
Heterogeneous Effects of Early Algebra across California Middle Schools: Andrew McEachin, RAND
Brainstorm session on causal identification of SNAP participation on food insecurity in low-income neighborhoods both before and during COVID-19: Jonathan Cantor, RAND
Equity and Allocation of Financial Aid in New Jersey: Drew Anderson, RAND

2019

Using Machine Learning for Causal Inference: a Focus on Implications for Policy: Lou Mariano, Brian Vegetabile, and Claude Setodji
Maximizing the value of qualitative data for causal inference with new methods for system dynamics simulation modeling: Andrada Tomoaia-Cotisel and Jason Etchegaray, RAND
Disability Program Participation and Student Loan Discharge: Melanie Zaber, RAND
Making Sense of Sensitivity: Extending Omitted Variable Bias: Carlos Cinelli, UCLA
Panel Data Inference with Dependent Clusters: David Powell, RAND
Incorporating Exogenous Variation into Machine Learning Techniques: Isaac Opper, RAND
Estimating the Long-term Impacts of Public Transit Projects in Los Angeles: A Case Study of the Expo Line: Diogo Prosdocimi, Pardee RAND

2018

A Generalizability Index for Regression Discontinuity Designs: Drew Anderson, RAND
Causal Inference in the Presence of Interference: Michael Hudgens, UNC Chapel Hill
Approaches for Estimating a High-Dimensional Regression Discontinuity: Isaac Opper, John Engberg, and Bill Johnston, RAND
Using Administrative Data to Identify an Appropriate Control Group for Veterans who use Complementary and Integrative Therapies: Matt Cefalu and Patricia Herman, RAND
Synthetic Control Methods with Micro- and Meso-Level Data in R: Michael Robbins, RAND
Imperfect Synthetic Controls: Did the Massachusetts Health Care Reform Save Lives?: David Powell, RAND
Data Confessions with Minimal Torture: The Info-Metrics Way: Amos Golan, American University

2017

The Effect of Unconditional Cash Transfers on the Return to Work of Permanently Disabled Workers: Kathleen Mullen and Stephanie Rennane, RAND
What do you do when you only have 1 treated unit and the synthetic control method fails?: Beth Ann Griffin, Priscilla Hunt, Layla Parast, and David Powell, RAND
Brainstorming the limitations and potential improvements of converting effect sizes of educational interventions into “days of learning” or similar metrics: Matthew Baird and John Pane, RAND
Assessing the likelihood of truth with Markov logic networks: John Raffensperger and Kenneth Kuhn, RAND
Causal Forensic Analysis – A structured, causal approach to analyzing challenging policy problems: Nick Burger and Radha Iyengar, RAND
Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects: P. Richard Hahn, University of Chigaco Booth School of Business

2016

Methods for Matching Social Media Data with Survey Data: Osonde Osoba, RAND
How Transparency and Reproducibility Can Connect Research and Policy: A Case Study of the Minimum Wage Policy Estimates: Fernando Hoces de la Guardia, Pardee RAND
Outcome-Optimized Nonresponse Weighting: Bonnie Ghosh-Dastidar and Terry Schell, RAND
Endogenous Stratification in Randomized Experiments: Alberto Abadie, MIT
Intention-to-treat analysis in partially nested randomized controlled trials with real-world complexity: Jon Schweig and John Pane, RAND
Inference with Difference-In-Difference with a small Number of Policy Changes: An evaluation of Random Effects and Discrete Mixture Models: Italo Gutierrez and John Engberg, RAND
Dealing with Variation in Test Conditions When Estimating Program Effects: Matt Baird, RAND
A Novel Application of Propensity Scores Reweighting to Evaluate a Nationwide Sterilization Campaign in Peru: Italo Gutierrez, RAND

2015

Instrumental variables for time-to-event data: Matt Cefalu, RAND
Assessing Covariate Balance for the Purpose of Identifying Potential Bias in Causal Effect Estimation: Terry Schell
Estimation and inference in panel data models with additive unobserved individual specific heterogeneity in a high dimensional setting: Chris Hansen, University of Chicago Booth School of Business
Individual Rights and Public Policy: A causal analysis of the effects of state supreme court decisions on electronic surveillance: Annie Boustead, Pardee RAND
Estimating the Impact of State-level Policy Variables: John Engberg and Hao Yu, RAND
Big Data and Social Science Research: Lessons from Financial Data: Justin McCrary, UC Berkeley

2014

Synthetic Control Estimation Beyond Case Studies: David Powell, RAND
Using Propensity Weighting Estimators when Treatment Identity is Unknown: An application to a National Female Sterilization Program in Peru: Italo Gutierrez, RAND
Propensity score methods in the context of covariate measurement error: Elizabeth Stuart, Johns Hopkins Bloomberg School of Public Health
Brainstorming Session: How do we determine the relative causal impact of several predictor variables?: Nelson Lim and Beth Ann Griffin, RAND
Brainstorming Session for PCORI Methods Application: Justin Timbie, Beth Ann Griffin, and Claude Setodji
Dynamic Program Evaluation: Sebastian Bauhoff, Lane Burgett and Michael Robbins, RAND
Landmark Estimation of Survival and Treatment Effect in an Observational Setting: Layla Parast, RAND
Causal Difference-in-Differences Estimators: Some Issues in Practice: Bing Han, RAND

How best to estimate the cost of foster home networks when compared to nursing homes in the US veteran population?

Kathryn Connor, RAND