## Causal Inference for Statistics, Social, and Biomedical Sciences: An IntroductionMost questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a subject were exposed to a particular treatment or regime. In this approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular subject. The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies. They lay out the assumptions needed for causal inference and describe the leading analysis methods, including matching, propensity-score methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher. |

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### Inhalt

A Brief History of the Potential Outcomes Approach | 23 |

A Classification of Assignment Mechanisms | 31 |

Neymans 1923 Potential Outcome Notation in Randomized | 47 |

Fishers Exact PValues for Completely Randomized Experiments | 57 |

Neymans Repeated Sampling Approach to Completely | 83 |

Regression Methods for Completely Randomized Experiments | 113 |

3 | 123 |

ModelBased Inference for Completely Randomized Experiments | 141 |

Estimating the Propensity Score | 281 |

Assessing Overlap in Covariate Distributions | 309 |

Matching to Improve Balance in Covariate Distributions | 337 |

Trimming to Improve Balance in Covariate Distributions | 359 |

Subclassification on the Propensity Score | 377 |

Matching Estimators | 401 |

A General Method for Estimating Sampling Variances | 433 |

Inference for General Causal Estimands | 461 |

15 | 145 |

18 | 156 |

Stratified Randomized Experiments | 187 |

2 | 215 |

Pairwise Randomized Experiments | 219 |

An Experimental Evaluation of a Labor Market | 240 |

24 | 251 |

Unconfounded Treatment Assignment | 257 |

Assessing Unconfoundedness | 479 |

Sensitivity Analysis and Bounds | 496 |

Instrumental Variables Analysis of Randomized Experiments with | 513 |

Instrumental Variables Analysis of Randomized Experiments | 542 |

Conclusions and Extensions | 589 |

Author Index | 605 |

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Causal Inference in Statistics, Social, and Biomedical Sciences Guido W. Imbens,Donald B. Rubin Eingeschränkte Leseprobe - 2015 |

### Häufige Begriffe und Wortgruppen

ˆτ active treatment analysis aspirin assessing assignment vectors assumption average effect average treatment effect barbiturate Bernoulli trial blocks calculate causal effects chapter classes classical randomized experiments completely randomized experiment conditional distribution confidence intervals consider control group control units covariance matrix covariate distributions discuss earnings equal estimated propensity score example FEP approach finite sample Fisher focus i:Wi imputation joint distribution large samples likelihood function matching methods missing potential outcomes Neyman normal normal distribution null hypothesis observational studies observed data observed outcomes p-value pair parameters posterior distribution pre-treatment variables prior distribution quantile regression function Rubin sampling variance Section ſº specification standard deviation strata stratified randomized experiments stratum super-population Table Tdif test statistic treated and control treated units treatment assignment treatment group treatment indicator treatment status unbiased unconfoundedness unit-level Yiobs Ymis Yobs Yobsc Yobsi Yobst zero