Estimating causal effects of treatments in randomized and nonrandomized studies.

1974/10/01 by Donald B. Rubin · 459 citations
Mathematics · Social Sciences · #Advanced Causal Inference Techniques #School Choice and Performance #Statistical Methods and Bayesian Inference

paper · doi:10.1037/h0037350

Abstract

Presents a discussion of matching, randomization, random sampling, and other methods of controlling extraneous variation. The objective was to specify the benefits of randomization in estimating causal effects of treatments. It is concluded that randomization should be employed whenever possible but that the use of carefully controlled nonrandomized data to estimate causal effects is a reasonable and necessary procedure in many cases.

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• Harnessing naturally randomized transcription to infer regulatory relationships among genes. [europepmc]
• Identifiability, exchangeability and confounding revisited. [europepmc]
• An introduction to causal inference. [europepmc]
• Inferring the conservative causal core of gene regulatory networks. [europepmc]
• Principal stratification--uses and limitations. [europepmc]
• Statistical inference and reverse engineering of gene regulatory networks from observational expression data. [europepmc]
• Statistical approaches for enhancing causal interpretation of the M to Y relation in mediation analysis. [europepmc]
• Estimating causal effects: considering three alternatives to difference-in-differences estimation. [europepmc]
• Design approaches to experimental mediation. [europepmc]
• Methods for time-varying exposure related problems in pharmacoepidemiology: An overview. [europepmc]
• Statistical methods to compare functional outcomes in randomized controlled trials with high mortality. [europepmc]
• Opportunities and obstacles for deep learning in biology and medicine. [europepmc]
• Statistics for Evaluating Pre-post Change: Relation Between Change in the Distribution Center and Change in the Individual Scores. [europepmc]
• Inferring causation from time series in Earth system sciences. [europepmc]
• Clarifying causal mediation analysis for the applied researcher: Defining effects based on what we want to learn. [europepmc]
• Supervised Machine Learning: A Brief Primer. [europepmc]
• Exploring the Immediate Effects of COVID-19 Containment Policies on Crime: an Empirical Analysis of the Short-Term Aftermath in Los Angeles. [europepmc]
• Application of an analytical framework for multivariate mediation analysis of environmental data. [europepmc]
• Assessing causality in epidemiology: revisiting Bradford Hill to incorporate developments in causal thinking. [europepmc]
• Using Propensity Scores for Causal Inference: Pitfalls and Tips. [europepmc]
• Propensity score matching with R: conventional methods and new features. [europepmc]
• Reporting of Observational Studies Explicitly Aiming to Emulate Randomized Trials: A Systematic Review. [europepmc]
• Causal identification of single-cell experimental perturbation effects with CINEMA-OT. [europepmc]
• Learning representations for image-based profiling of perturbations. [europepmc]
• The Target Trial Framework for Causal Inference From Observational Data: Why and When Is It Helpful? [europepmc]

Related

• Decomposing Treatment Effect Variation
• Randomization Inference for Treatment Effect Variation
• Causally-Interpretable Random-Effects Meta-Analysis
• Notes on Causation, Comparison, and Regression
• Causal inference from treatment-control studies having an additional factor with unknown assignment mechanism