Marc Ratkovic

Department of Politics

Princeton University

Recent Papers of Interest

Legislative institutions structure and order the myriad topics addressed by legislators. Jointly considering both roll call votes and floor speech, we show that the contemporary US Senate is ordered around but two dimensions: one ideological and another capturing leadership.  We characterize both word and vote choice in terms of the exact same ideal points and policy dimensions.  These findings emerge from our method, Sparse Factor Analysis (SFA), designed to combine vote and textual data when estimating ideal points, word affect, and the underlying dimensionality. This contrasts with the single dimension that emerges from an analysis of votes alone, and with the more numerous dimensions that emerge from analyzing speech alone. We then show how SFA can leverage common speech in order to impute missing data, to estimate rank-and-file ideal points using only their words and the vote history of party leaders, and even to scale newspaper editorials.  

With In Song Kim and John Londregan.

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

My research interests include causal inference, machine learning methods, and text analysis.  

I teach courses in political methodology at the undergraduate and graduate level.

A brief background of myself and my life.  Sometiemes I even do things that aren't political science.



About Me

Matching and weighting methods are commonly used to reduce confounding bias in observational studies. Many existing methods are sensitive to user-provided inputs, provide little formal guidance in selecting these inputs, and do not necessarily return a balanced subset of the data. The proposed method adapts the support vector machine classifier in order to provide a fully automated, nonparametric procedure for identifying the largest balanced subset of the data. The method allows for a sensitivity analysis and an assessment of the common support assumption. Two applications, a simulation study and a benchmark dataset, illustrate the method's use and efficacy.

Implemented through SVMMatch package in the R programming lanuage (link).

Balancing within the Margin: Causal Effect Estimation with

Support Vector Machines

Experimental designs, from the simple experiment to a conjoint analysis, allow straightforward estimation of one or more average treatment effects. Modeling higher-order interactions between treatments and covariates may require considering many possible variables. We implement a set of methods designed to help the researcher conduct just such an analysis. The method, which we label LASSOplus,  identifies relevant subgroups in the data while also returning uncertainty estimates for each subgroup effect. This is accomplished via a Bayesian estimation strategy that couples variable selection and effect estimation into a single framework.  Our software, which will be made publicly available, formats and pre-processes data; generates diagnostic and summary plots; accommodates binary, ordinal, categorical, and truncated outcomes; and models clustered data with repeated observations.  We illustrate the methods, software, and diagnostics through simulation studies as well as an analysis of a recent conjoint experiment estimating public support for climate change treaties as a function of different treaty design decisions.

With Dustin Tingley

Implemented through sparsereg package in the R programming lanuage (link).

Sparse Estimation with Uncertainty: Subgroup Analysis in Large Dimensional Designs

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Recent Papers of Interest