Counterfactuals and Causal Inference: Methods and Principles for Social ResearchDid mandatory busing programs in the 1970s increase the school achievement of disadvantaged minority youth? Does obtaining a college degree increase an individual's labor market earnings? Did the use of the butterfly ballot in some Florida counties in the 2000 presidential election cost Al Gore votes? If so, was the number of miscast votes sufficiently large to have altered the election outcome? At their core, these types of questions are simple cause-and-effect questions. Simple cause-and-effect questions are the motivation for much empirical work in the social sciences. This book presents a model and set of methods for causal effect estimation that social scientists can use to address causal questions such as these. The essential features of the counterfactual model of causality for observational data analysis are presented with examples from sociology, political science, and economics. |
Contents
Chapter 2 | 31 |
Chapter 3 | 61 |
t | 67 |
Chapter 4 | 87 |
Chapter 5 | 123 |
Chapter 6 | 169 |
Chapter 7 | 187 |
Chapter 8 | 219 |
Chapter 9 | 243 |
D | 251 |
Chapter 10 | 277 |
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Common terms and phrases
adjustment alternative analysis of covariance Angrist association assumed assumption average causal effect average treatment effect back-door criterion back-door path bias Catholic school causal diagram causal effect estimate causal inference causal variable causes Chapter coefficient collider compliers conditional expectation consider consistent estimate control group correlation counterfactual model dataset defined discuss distribution equal Equation error term estimated propensity scores Figure fixed effect model function Goldthorpe graph Heckman heterogeneity hypothetical example identify individual-level causal effect individuals least squares linear literature Manski Matching Demonstration matching estimators mechanism naive estimator never takers observational data observed outcome omitted-variable bias Operation Ceasefire outcome variable Pearl’s perfect stratification population potential outcomes Pr[S present private schools random variables regression estimator regression model researcher result Rosenbaum sample social sciences specification Subsection treated treatment and control treatment assignment treatment group treatment selection unbiased and consistent unconditional average treatment unobserved untreated values voucher weighting
References to this book
Design and Analysis of Time-series Experiments Gene V Glass,Victor L. Willson,John Mordechai Gottman No preview available - 2008 |
Cumulative Social Inquiry: Transforming Novelty Into Innovation Robert Benjamin Smith No preview available - 2008 |