## Counterfactuals and Causal InferenceIn this second edition of Counterfactuals and Causal Inference, completely revised and expanded, the essential features of the counterfactual approach to observational data analysis are presented with examples from the social, demographic, and health sciences. Alternative estimation techniques are first introduced using both the potential outcome model and causal graphs; after which, conditioning techniques, such as matching and regression, are presented from a potential outcomes perspective. For research scenarios in which important determinants of causal exposure are unobserved, alternative techniques, such as instrumental variable estimators, longitudinal methods, and estimation via causal mechanisms, are then presented. The importance of causal effect heterogeneity is stressed throughout the book, and the need for deep causal explanation via mechanisms is discussed. |

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### Contents

Introduction | 3 |

Counterfactuals and the Potential Outcome Model | 37 |

Causal Graphs | 77 |

Estimating Causal Effects by Conditioning on Observed Variables to Block | 103 |

Matching Estimators of Causal Effects | 140 |

Regression Estimators of Causal Effects | 188 |

Weighted Regression Estimators of Causal Effects | 226 |

SelfSelection Heterogeneity and Causal Graphs | 267 |

Instrumental Variable Estimators of Causal Effects | 291 |

Mechanisms and Causal Explanation | 325 |

Repeated Observations and the Estimation of Causal Effects | 354 |

Estimation When Causal Effects Are Not PointIdentified by Observables | 417 |

Counterfactuals and the Future of Empirical Research in Observational | 437 |

451 | |

497 | |

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adjustment alternative Angrist assumed assumptions average causal effect average treatment effect back-door criterion back-door paths bias Catholic school causal diagram causal effect estimate causal graph causal variable causes chapter charter schools coefficient collider compliers conditional expectations confounding consider consistent and unbiased consistently estimate control group counterfactual approach dataset defined directed graph discuss distribution equal error term estimated propensity scores example explain graph in Figure Heckman heterogeneity identified individual-level causal effect individuals instrumental variable least squares linear literature Matching Demonstration matching estimators mechanism naive estimator observed data observed variables omitted-variable bias Operation Ceasefire outcome variable panel panel data parameter Pearl point estimates population potential outcome model Pr[S present public schools regression models result sample school students self-selection social sciences specification standard errors strategy Table test scores treatment and control treatment assignment treatment group treatment selection unbiased estimates unblocked unobserved values weighted regression estimators Y0it