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Dive into the research topics where Marc Ratkovic is active.

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Featured researches published by Marc Ratkovic.


The Annals of Applied Statistics | 2013

Estimating treatment effect heterogeneity in randomized program evaluation

Kosuke Imai; Marc Ratkovic

When evaluating the efficacy of social programs and medical treatments using randomized experiments, the estimated overall average causal effect alone is often of limited value and the researchers must investigate when the treatments do and do not work. Indeed, the estimation of treatment effect heterogeneity plays an essential role in (1) selecting the most effective treatment from a large number of available treatments, (2) ascertaining subpopulations for which a treatment is effective or harmful, (3) designing individualized optimal treatment regimes, (4) testing for the existence or lack of heterogeneous treatment effects, and (5) generalizing causal effect estimates obtained from an experimental sample to a target population. In this paper, we formulate the estimation of heterogeneous treatment effects as a variable selection problem. We propose a method that adapts the Support Vector Machine classifier by placing separate sparsity constraints over the pre-treatment parameters and causal heterogeneity parameters of interest. The proposed method is motivated by and applied to two well-known randomized evaluation studies in the social sciences. Our method selects the most effective voter mobilization strategies from a large number of alternative strategies, and it also identifies the characteristics of workers who greatly benefit from (or are negatively affected by) a job training program. In our simulation studies, we find that the proposed method often outperforms some commonly used alternatives.


Journal of the American Statistical Association | 2015

Robust Estimation of Inverse Probability Weights for Marginal Structural Models

Kosuke Imai; Marc Ratkovic

Marginal structural models (MSMs) are becoming increasingly popular as a tool for causal inference from longitudinal data. Unlike standard regression models, MSMs can adjust for time-dependent observed confounders while avoiding the bias due to the direct adjustment for covariates affected by the treatment. Despite their theoretical appeal, a main practical difficulty of MSMs is the required estimation of inverse probability weights. Previous studies have found that MSMs can be highly sensitive to misspecification of treatment assignment model even when the number of time periods is moderate. To address this problem, we generalize the covariate balancing propensity score (CBPS) methodology of Imai and Ratkovic to longitudinal analysis settings. The CBPS estimates the inverse probability weights such that the resulting covariate balance is improved. Unlike the standard approach, the proposed methodology incorporates all covariate balancing conditions across multiple time periods. Since the number of these conditions grows exponentially as the number of time period increases, we also propose a low-rank approximation to ease the computational burden. Our simulation and empirical studies suggest that the CBPS significantly improves the empirical performance of MSMs by making the treatment assignment model more robust to misspecification. Open-source software is available for implementing the proposed methods.


Journal of The Royal Statistical Society Series B-statistical Methodology | 2014

Covariate Balancing Propensity Score

Kosuke Imai; Marc Ratkovic


Political Analysis | 2017

Sparse Estimation and Uncertainty with Application to Subgroup Analysis

Marc Ratkovic; Dustin Tingley


Political Analysis | 2010

Finding Jumps in Otherwise Smooth Curves: Identifying Critical Events in Political Processes

Marc Ratkovic; Kevin H. Eng


Archive | 2015

Voting, Speechmaking, and the Dimensions of Conict in the US Senate

In Song Kim; John Londregan; Marc Ratkovic


Political Analysis | 2018

Estimating Spatial Preferences from Votes and Text

In Song Kim; John Londregan; Marc Ratkovic


arXiv: Machine Learning | 2017

Causal Inference through the Method of Direct Estimation

Marc Ratkovic; Dustin Tingley


Archive | 2017

Replication Data for: "Estimating Spatial Preferences from Votes and Text"

In Song Kim; John Londregan; Marc Ratkovic


Archive | 2015

Bicoordinate Descent for the LASSO

In Song Kim; John Londregan; Marc Ratkovic

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In Song Kim

Massachusetts Institute of Technology

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Kevin H. Eng

Roswell Park Cancer Institute

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