IDENTIFYING MECHANISMS BEHIND POLICY INTERVENTIONS VIA CAUSAL MEDIATION ANALYSIS

Causal analysis in program evaluation has primarily focused on the question about whether or not a program, or package of policies, has an impact on the targeted outcome of interest. However, it is often of scientific and practical importance to also explain why such impacts occur. In this paper, we introduce causal mediation analysis, a statistical framework for analyzing causal mechanisms that has become increasingly popular in social and medical sciences in recent years. The framework enables us to show exactly what assumptions are sufficient for identifying causal mediation effects for the mechanisms of interest, derive a general algorithm for estimating such mechanism-specific effects, and formulate a sensitivity analysis for the violation of those identification assumptions. We also discuss an extension of the framework to analyze causal mechanisms in the presence of treatment noncompliance, a common problem in randomized evaluation studies. The methods are illustrated via applications to two intervention studies on pre-school classes and job-training workshops

Modelling and Using Response Times in Online Courses

Each time a learner in a self-paced online course seeks to answer an assessment question, it takes some time for the student to read the question and arrive at an answer to submit. If multiple attempts are allowed, and the first answer is incorrect, it takes some time to provide a second answer. Here we study the distribution of such "response times." We find that the log-normal statistical model for such times, previously suggested in the literature, holds for online courses. Users who, according to this model, tend to take longer on submits are more likely to complete the course, have a higher level of engagement, and achieve a higher grade. This finding can be the basis for designing interventions in online courses, such as MOOCs, which would encourage "fast" users to slow down

stm: An R Package for Structural Topic Models

This paper demonstrates how to use the R package stm for structural topic modeling. The structural topic model allows researchers to flexibly estimate a topic model that includes document-level metadata. Estimation is accomplished through a fast variational approximation. The stm package provides many useful features, including rich ways to explore topics, estimate uncertainty, and visualize quantities of interest

mediation: R package for causal mediation analysis

In this paper, we describe the R package mediation for conducting causal mediation analysis in applied empirical research. In many scientific disciplines, the goal of researchers is not only estimating causal effects of a treatment but also understanding the process in which the treatment causally affects the outcome. Causal mediation analysis is frequently used to assess potential causal mechanisms. The mediation package implements a comprehensive suite of statistical tools for conducting such an analysis. The package is organized into two distinct approaches. Using the model-based approach, researchers can estimate causal mediation effects and conduct sensitivity analysis under the standard research design. Furthermore, the design-based approach provides several analysis tools that are applicable under different experimental designs. This approach requires weaker assumptions than the model-based approach. We also implement a statistical method for dealing with multiple (causally dependent) mediators, which are often encountered in practice. Finally, the package also offers a methodology for assessing causal mediation in the presence of treatment noncompliance, a common problem in randomized trials

Anti-Americanism in Arabic Twitter discourses is driven by perceptions of U.S. impingement in the region.

Recent years have seen very public expressions of anti-American sentiment in many parts of the Middle East. But are these sentiments driven by American actions in the region, or by the country itself? In new research, Amaney A. Jamal, Robert O. Keohane, David Romney, and Dustin Tingley use data from Arabic Twitter from 2012 and 2013 to investigate anti-American attitudes. By looking at reactions to the potential for U.S. intervention in situations such as the Syrian Civil War, they find that in the Arabic Twitter world, negative attitudes are driven by the perceptions of potential American social and political impingement. They also find that these negative attitudes can be directed at other impinging powers, such as Iran

Creating Climate Coalitions: Mass Preferences for Compensating Vulnerability in the World’s Two Largest Democracies

Combating climate change requires large economic adjustments with significant distributional implications. To build coalitions of support, scholars and policymakers propose compensating individuals who will bear decarbonization's costs. What are the determinants of public opinion regarding climate compensation and investment? We theorize that climate policy vulnerability and climate change vulnerability induce support for distinct types of climate policy. Fielding original surveys in the United States and India, we show that people who reside in coal-producing regions prefer compensation for lost jobs. The general public privileges diffuse redistribution mechanisms and investments, discounting compensation to targeted groups. Those who are both physically and economically vulnerable have cross-cutting preferences. Nevertheless, there is considerable support across our samples for policies that compensate different coalitions of climate vulnerable citizens, in line with theories of `just energy' transition and embedded liberalism. We trace the distinctive compensatory preferences of fossil fuel communities to a logic of shared community identities

Belief Updating in Sequential Games of Two-Sided Incomplete Information: An Experimental Study of a Crisis Bargaining Model

We investigate theoretically and experimentally the crisis bargaining model, a dynamic game of two-sided incomplete information with player types drawn from a commonly known distribution. Little work has been done to analyze whether and how players update their beliefs in such games.Within the experiment we elicited beliefs from players about their opponent's type using a proper scoring rule. We implement two treatments that vary the cost of backing down to the first mover after initial entry, generating sharp comparative static predictions in both beliefs and strategies. We find that players do update their beliefs in the predicted directions after observing some of the action choices. However, we highlight evidence of conservative updating relative to rational expectations