### Abstract

This article discusses causal inference in statistics. It describes the theoretical framework and notation needed to formally define causal effects and the assumptions required to identify them nonparametrically. This involves definition of potential outcomes that represent the potential value of the outcome across different treatment exposures. Designs that allow researchers to satisfy or weaken these assumptions are briefly described. Then common parametric assumptions used to model effects and more current approaches that require weaker assumptions are discussed.

Original language | English (US) |
---|---|

Title of host publication | International Encyclopedia of the Social & Behavioral Sciences: Second Edition |

Publisher | Elsevier Inc. |

Pages | 255-260 |

Number of pages | 6 |

ISBN (Electronic) | 9780080970875 |

ISBN (Print) | 9780080970868 |

DOIs | |

State | Published - Mar 26 2015 |

### Fingerprint

### Keywords

- Causal inference
- Common support
- Ignorability
- Observational studies
- Overlap
- Potential outcomes
- Propensity scores
- Quasi-experiments
- Randomized experiments
- Regression
- SUTVA

### ASJC Scopus subject areas

- Social Sciences(all)

### Cite this

*International Encyclopedia of the Social & Behavioral Sciences: Second Edition*(pp. 255-260). Elsevier Inc.. https://doi.org/10.1016/B978-0-08-097086-8.42095-7

**Causal Inference : Overview.** / Hill, Jennifer; Stuart, Elizabeth.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

*International Encyclopedia of the Social & Behavioral Sciences: Second Edition.*Elsevier Inc., pp. 255-260. https://doi.org/10.1016/B978-0-08-097086-8.42095-7

}

TY - CHAP

T1 - Causal Inference

T2 - Overview

AU - Hill, Jennifer

AU - Stuart, Elizabeth

PY - 2015/3/26

Y1 - 2015/3/26

N2 - This article discusses causal inference in statistics. It describes the theoretical framework and notation needed to formally define causal effects and the assumptions required to identify them nonparametrically. This involves definition of potential outcomes that represent the potential value of the outcome across different treatment exposures. Designs that allow researchers to satisfy or weaken these assumptions are briefly described. Then common parametric assumptions used to model effects and more current approaches that require weaker assumptions are discussed.

AB - This article discusses causal inference in statistics. It describes the theoretical framework and notation needed to formally define causal effects and the assumptions required to identify them nonparametrically. This involves definition of potential outcomes that represent the potential value of the outcome across different treatment exposures. Designs that allow researchers to satisfy or weaken these assumptions are briefly described. Then common parametric assumptions used to model effects and more current approaches that require weaker assumptions are discussed.

KW - Causal inference

KW - Common support

KW - Ignorability

KW - Observational studies

KW - Overlap

KW - Potential outcomes

KW - Propensity scores

KW - Quasi-experiments

KW - Randomized experiments

KW - Regression

KW - SUTVA

UR - http://www.scopus.com/inward/record.url?scp=85043427662&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85043427662&partnerID=8YFLogxK

U2 - 10.1016/B978-0-08-097086-8.42095-7

DO - 10.1016/B978-0-08-097086-8.42095-7

M3 - Chapter

AN - SCOPUS:85043427662

SN - 9780080970868

SP - 255

EP - 260

BT - International Encyclopedia of the Social & Behavioral Sciences: Second Edition

PB - Elsevier Inc.

ER -