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

Brownbag Seminars


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


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


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


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


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


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


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


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