Statistics and Causal Inference: A Review
نویسنده
چکیده
This paper aims at assisting empirical researchers benefit from recent advances in causal inference. The paper stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, and the conditional nature of causal claims inferred from nonexperimental studies. These emphases are illustrated through a brief survey of recent results, including the control of confounding, the assessment of causal effects, the interpretation of counterfactuals, and a symbiosis between counterfactual and graphical methods of analysis.
منابع مشابه
Review of the book “ Causal Inference for Statistics , Social , and Biomedical Sciences ” by G . W . Imbens and D . B . Rubin
Research questions that motivate most studies in statistics-based sciences are causal in nature. Economists and social scientists are typically interested in estimating causal effects rather than mere associations between variables (e.g., the effects of training programs on subsequent labor market histories); the same is true for epidemiologists and medical doctors (e.g., is smoking causing lun...
متن کاملTom Ten Have's contributions to causal inference and biostatistics: review and future research directions.
Tom Ten Have made many contributions to causal inference and biostatistics before his untimely death. This paper reviews Tom's contributions and discusses potential related future research directions. We focus on Tom's contributions to longitudinal/repeated measures categorical data analysis and particularly his contributions to causal inference. Tom's work on causal inference was primarily in ...
متن کاملThe Statistics of Causal Inference: The View from Political Methodology
Many areas of political science focus on causal questions. Evidence from statistical analyses are often used to make the case for causal relationships. While statistical evidence can help establish causal relationships, it can also provide strong evidence of causality where none exists. In this essay, I provide an overview of the statistics of causal inference. Instead of focusing on statistica...
متن کاملInterference and Sensitivity Analysis.
Causal inference with interference is a rapidly growing area. The literature has begun to relax the "no-interference" assumption that the treatment received by one individual does not affect the outcomes of other individuals. In this paper we briefly review the literature on causal inference in the presence of interference when treatments have been randomized. We then consider settings in which...
متن کامل