Causal Inference Seed Grants

The Center for Causal Inference regularly provides seed funds to researchers and Pardee RAND students to develop and improve upon causal inference methods—and in turn improve our ability to effectively analyze policy.

2022

• Daniel Schwam and Jessie Coe: Estimating the Distribution of Labor Market Effects of Hurricanes via Synthetic Control Method
• Peter Hudomiet: Developing a New Approach to Estimate the Causal Effect of education: An Application for Dementia
• Maria DeYoreo: Differences Between Fixed and Random Effects in Regression Models and Guidelines for Use

2021

• Emma Thomas: Longitudinal Mechanisms in Health Research
• Brian Vegetabile: Developing Scalable Methods and Software for Optimal Balancing Based Causal Effect Estimation

2020

• Denis Agniel: Doubly Robust Functional Synthetic Control Estimation
• Jason Ward: Willingness-to-Pay for School Quality - An Application of Spatial First Differences

2019

• Maria DeYoreo and Jonathan Cantor: Estimating the Effect of Policies and Treatment Facility Characteristics on Substance Abuse Treatment Capacity and Health Outcomes
• Andrew McEachin: Tipping Point Method for RD
• Rachel Perera (Pardee RAND): Estimating Teacher Effectiveness in the Presence of Multiple Factors
• Andrara Tomoaia-Cotisel and Jason Etchegaray: Maximizing the Value of Qualitative Data for Causal Inference with New Methods for System Dynamics Simulation Modeling

2018

• Drew Anderson: Generalizability Index for Regression Discontinuity
• Isaac Opper: Combining Observational Data and Experimental Variation to Estimate Treatment Effects
• Diogo Prosdocimi (Pardee RAND): Estimating the Long-term Impacts of Public Transit Projects in Los Angeles on Traffic Congestion: A Case Study of the ExpoLine.

2017

• Nick Burger and Radha Iyengar: Developing a Systematic Causal Forensic Analysis Approach
• David Powell: Two-Step Synthetic Control Procedure
• John Raffensperger and Ken Kuhn: Assessing the Validity of Shared Information with Fuzzy Inference
• Michael Robbins: An R Package for SCM with Microlevel Data

2016

• Kristine Brown: Increasing the Research Value of Policy Experiments
• Michael Dworsky: Analytic Cluster-Robust Variance Estimation for the Two-Sample Two-Stage Least Squares Estimator
• Bing Han and Matt Cefalu: Model Averaging Approach for Estimating the Efficacy of an Intervention Subject to Non-Compliance
• Osonde Osoba: Methods for Matching Social Media Data with Survey Data (co-funded by CCI and SCAN and the Center for Social Networking and Systems)

2015

• Lane Burgette: TWANG for Big Data
• Italo Gutierrez and John Engberg: Monte Carlo Simulations to Evaluate Two Alternative Estimators
• Paul Heaton and Matt Cefalu: Estimating Duration Models with Endogenous Explanatory Variables

2014

• Italo Gutierrez: Estimating Causal Effects When the Identity of the Treated Group is Unknown
• David Powell: Generalized Quantile Regression Code
• Hao Yu and John Engberg: The Causal Effects of State-level Tobacco Control Policies

2013

• Beth Ann Griffin and Claude Setodji: Chasing Balance in Propensity Score Methods
• Bing Han and Hao Yu: A Causal Difference-in-Differences Approach for Zero-Inflated Outcomes
• Layla Parast: Improvement of the Efficiency of Treatment Effect Estimation for Time-to-Event Outcomes Using Intermediate Events and Observational Data