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A Practical Introduction to Regression Discontinuity Designs
In this Element, which continues our discussion in Foundations, the authors provide an accessible and practical guide for the analysis and interpretation of Regression Discontinuity (RD) designs that encourages the use of a common set of practices and facilitates the accumulation of RD-based empirical evidence. The focus is on extensions to the canonical sharp RD setup that we discussed in Foundations. The discussion covers (i) the local randomization framework for RD analysis, (ii) the fuzzy RD design where compliance with treatment is imperfect, (iii) RD designs with discrete scores, and (iv) and multi-dimensional RD designs.
A Practical Introduction to Regression Discontinuity Designs
In this Element and its accompanying second Element, A Practical Introduction to Regression Discontinuity Designs: Extensions, Matias Cattaneo, Nicolás Idrobo, and Rocıìo Titiunik provide an accessible and practical guide for the analysis and interpretation of regression discontinuity (RD) designs that encourages the use of a common set of practices and facilitates the accumulation of RD-based empirical evidence. In this Element, the authors discuss the foundations of the canonical Sharp RD design, which has the following features: (i) the score is continuously distributed and has only one dimension, (ii) there is only one cutoff, and (iii) compliance with the treatment assignment is perfect. In the second Element, the authors discuss practical and conceptual extensions to this basic RD setup.
Regression Discontinuity Designs
Volume 38 of Advances in Econometrics collects twelve innovative and thought-provoking contributions to the literature on Regression Discontinuity designs, covering a wide range of methodological and practical topics such as identification, interpretation, implementation, falsification testing, estimation and inference.
Handbook of Matching and Weighting Adjustments for Causal Inference
An observational study infers the effects caused by a treatment, policy, program, intervention, or exposure in a context in which randomized experimentation is unethical or impractical. One task in an observational study is to adjust for visible pretreatment differences between the treated and control groups. Multivariate matching and weighting are two modern forms of adjustment. This handbook provides a comprehensive survey of the most recent methods of adjustment by matching, weighting, machine learning and their combinations. Three additional chapters introduce the steps from association to causation that follow after adjustments are complete. When used alone, matching and weighting do not use outcome information, so they are part of the design of an observational study. When used in conjunction with models for the outcome, matching and weighting may enhance the robustness of model-based adjustments. The book is for researchers in medicine, economics, public health, psychology, epidemiology, public program evaluation, and statistics who examine evidence of the effects on human beings of treatments, policies or exposures.
Demystifying Causal Inference
This book provides an accessible introduction to causal inference and data analysis with R, specifically for a public policy audience. It aims to demystify these topics by presenting them through practical policy examples from a range of disciplines. It provides a hands-on approach to working with data in R using the popular tidyverse package. High quality R packages for specific causal inference techniques like ggdag, Matching, rdrobust, dosearch etc. are used in the book. The book is in two parts. The first part begins with a detailed narrative about John Snow’s heroic investigations into the cause of cholera. The chapters that follow cover basic elements of R, regression, and an introdu...
The Effect
Extensive code examples in R, Stata, and Python Chapters on overlooked topics in econometrics classes: heterogeneous treatment effects, simulation and power analysis, new cutting-edge methods, and uncomfortable ignored assumptions An easy-to-read conversational tone Up-to-date coverage of methods with fast-moving literatures like difference-in-differences
Survival Analysis
Quantitative social scientists use survival analysis to understand the forces that determine the duration of events. This Element provides a guideline to new techniques and models in survival analysis, particularly in three areas: non-proportional covariate effects, competing risks, and multi-state models. It also revisits models for repeated events. The Element promotes multi-state models as a unified framework for survival analysis and highlights the role of general transition probabilities as key quantities of interest that complement traditional hazard analysis. These quantities focus on the long term probabilities that units will occupy particular states conditional on their current state, and they are central in the design and implementation of policy interventions.
Beyond Turnout
Beyond Turnout crafts a new theory that considers the downstream consequences of compulsory voting for both citizens and political parties. This theory is comprehensively tested through data from dozens of countries, with a particular focus on Argentina and Switzerland
Does Relative Deprivation Condition the Effects of Social Protection Programs on Political Support? Experimental Evidence from Pakistan∗
Could perceived relative economic standing affect citizens’ support for political leaders and institutions? We explore this question by examining Pakistan’s national unconditional cash transfer program, the Benazir Income Support Program (BISP). Leveraging a regression discontinuity approach using BISP’s administrative data and an original survey experiment, we find that perceptions of relative deprivation color citizen reactions to social protection. When citizens do not feel relatively deprived, receiving cash transfers has little sustained effect on individuals’ reported level of support for their political system and its leaders. However, when citizens feel relatively worse off, those receiving cash transfers become more politically satisfied, while those denied transfers become more politically disgruntled. Moreover, the magnitude of the reduction in political support among non-beneficiaries is larger than the magnitude of the increase in political support among beneficiaries. This has important implications for our understanding of the political ramifications of rising perceived inequality.
Violent Victors
Why populations brutalized in war elect their tormentors One of the great puzzles of electoral politics is how parties that commit mass atrocities in war often win the support of victimized populations to establish the postwar political order. Violent Victors traces how parties derived from violent, wartime belligerents successfully campaign as the best providers of future societal peace, attracting votes not just from their core supporters but oftentimes also from the very people they targeted in war. Drawing on more than two years of groundbreaking fieldwork, Sarah Daly combines case studies of victim voters in Latin America with experimental survey evidence and new data on postwar electio...
