Causal inference imbens rubin pdf

Guido imbens and don rubin present an insightful discussion of the potential outcomes framework for causal inference this book presents a unified framework to causal inference based on the potential outcomes framework, focusing on the classical analysis of experiments, unconfoundedness, and noncompliance. Identification of causal effects using instrumental variables. When statistical and econometric methodologists write about causal inference, they generally focus on forward causal questions. These books are not required, but most purchase them because we assume that you have access to them when needed. Imbens and rubin 2015 causal inference for statistics, social, and biomedical sciences, cambridge university press. In this introductory chapter we set out our basic framework for causal inference. Bayesian inference for causal effects in randomized experiments with noncompliance1 by guido w. Network causal inference on social media influence operations. Following the work by rubin 1977 causal questions are. Keywords propensity score causal effect causal inference potential outcome american statistical association. Kao, airoldi, and rubin, causal inference under network interference.

Kosuke imai harvard causal dags stat186gov2002 fall. Neyman rubin potential outcomes model intervention and outcome experimental units and potential outcomes causal effects treatment assignment mechanism and the propensity score inference of causal effects supplementary reading. We discuss three key notions underlying our approach. Frangakis department of biostatistics, johns hopkins university 615 n. Graphbased criteria of identi ability of causal questions shpitser 7. We use rubin causal model with the main assumptions of sutva, unconfoundedness, and overlap see imbens and rubin 2015 and rosenbaum and rubin 1983. Rubin department of statistics, harvard university 1 oxford st. A network potential outcome framework with bayesian imputation, in preparation kao, causal inference under network interference. Identification of causal effects using instrumental variables joshua d. We are taught to answer questions of the type what if. Rubin, most questions in social and biomedical sciences are. Pgjpearlglymourjewll weeks topics reading 1 introduction to causal modeling and counterfactuals.

For most of this century, randomization has been a cornerstone of scientific experimentation, especially when dealing with humans as experimental units. Causal inference using potential outcomes 323 on one occasion, fisher 1954 struck out blindly against a young lady who had been anything but offensive or incompetent. Rubin we outline a framework for causal inference in settings where assignment to a binary treatment is ignorable, but compliance with the assignment is not perfect so that the receipt of treatment is nonignorable. Basic concepts of statistical inference for causal effects in. For objective causal inference, design trumps analysis. Causal inference in statistics, social, and biomedical sciences. In this wonderful and important book, imbens and rubin give a lucid account of the. Recent developments in the econometrics of program evaluation. Rejoinder on causal inference through potential outcomes and. Let y i1 denote the potential outcome for unit i if the unit receives treatment, and let y. One of the attractions of the potential outcomes setup is that from. Social, and biomedical sciences, cambridge university press.

Imbens and rubin provide unprecedented guidance for designing research on causal relationships, and for interpreting the results of that research appropriately. Causal inference for statistics, social and biomedical sciences. It has also spurred the development of new tools for estimation and inference. Machine learning and causal inference for policy evaluation. Some of the more common nonexperimental study designs that fit within the rcm framework include. While their respective goals substantially overlap, methods from structural causal models tend to emphasize. Causal inference for statistics, social, and biomedical sciences. Pham large scale causal inference with machine learning 4 39. A fulllength text that discusses estimation and inference for causal effects from this perspective is imbens and rubin 2006. Download fulltext pdf download fulltext pdf read fulltext.

In practice, however, noncompliance is relatively common with human subjects, complicating traditional theories of inference that require adherence to the random treatment assignment. Causal inference m16 qingyuan zhao from its onset, modern statistics engages in the problem of inferring causality from data. Cambridge core statistical theory and methods causal inference for statistics, social, and biomedical. Guido imbens, donald rubin, causal inference for statistics. Rubin harvard university for most of this century, randomization has been a cornerstone of scienti c experimentation, especially when dealing with humans as experimental units. I bayesian inference considers the observed values of the four quantities to be realizations of random variables and the unobserved values to be unobserved random variables i pry0. Causal inference for statistics, social, and biomedical sciences, guido w. The neymanrubin model of causal inference and estimation via. A quick tour of modern causal inference methods 1 randomized experiments classical randomized experiments cluster randomized experiments instrumental variables 2 observational studies regression discontinuity design matching and weighting fixed effects and differenceindifferences 3 causal mechanisms direct and indirect effects causal. Causal inference based on the assignment mechanism design before outcome data. In section 5 we dis cuss how to evaluate the sensitivity of the iv. Request pdf causal inference for statistics, social and biomedical. Causal inference for statistics, social, and biomedical. No book can possibly provide a comprehensive description of methodologies for causal inference across the sciences.

Rubin and imbens summarize the voluminous literature on propensity score and related causal inference techniques in a manner that is accessible to someone with a solid background in statistics both frequentist and bayesian. Angrist, imbens and rubin 1996, imbens and rubin 1997, balke and pearl 1997 hirano et al. It is an introduction in the sense that it is 600 pages and still doesnt have room for differenceindifferences, regression discontinuity, synthetic controls. To address this deficiency, we propose a general framework for comparing treatments adjusting for posttreatment variables that yields principal effects based on principal stratification. Angrist and pischke 2009 mostly harmless econometrics, princeton university press. They used to sell books in pdf and then suddenly terminated the.

Sep 21, 2015 over the summer ive been slowly working my way through the new book causal inference for statistics, social, and biomedical sciences. Discovery is central to textbased causal inferences because text is complex and. Imbens specializes in econometrics, and in particular methods for drawing causal inferences. Please do not feel discouraged by this remark causal learning is a fascinating topic and we hope. In section 2 we briefly describe the structural equation approach to causal inference in economics.

The first notion is that of potential outcomes, each corresponding to one of the levels of a treatment or manipulation, following the dictum no causation without manipulation rubin, 1975, p. Studies with multiple partially controlled factors 1 partially controlled studies. Many scientific problems require that treatment comparisons be adjusted for posttreatment variables, but the estimands underlying standard methods are not causal effects. The statistics of causal inference in the social sciences. The rubin causal model has also been connected to instrumental variables angrist, imbens, and rubin, 1996 and other techniques for causal inference. Feb5,duefeb randomizedexperimentsfebruary12february21 whyrandomizationallowsidentificationofcausaleffects. The key to connecting the two traditions is recognizing the central role of discovery when using text data for causal inferences. These expressions are the same in each of the causal inference meth. Imbens, guido, rubin, donald, causal inference for statistics, social, and biomedical sciences. Causal inference in statistics, social and biomedical sciences. For more on the connections between the rubin causal model, structural equation modeling, and other statistical methods for causal inference, see morgan and winship 2007.

Methods and principles for social research, cambridge university press. Rubin s formulation of the evaluation problem, or the problem of causal inference, labeled the rubin causal model rcm by holland 1986, is by now standard in both the statistics and econometrics literature. I causal inference under the potential outcome framework is essentiallya missing data problem i to identify causal effects from observed data, one must. Guido imbens is a fellow of the econometric society and the american academy of arts and sciences. In particular, the same underlying principles can be used to design both types of studies, and the thesis of this article is that for objective causal inference, those principles must be used. Apr 02, 2020 after graduating from brown university guido taught at harvard university, ucla, and uc berkeley. In this paper we present bayesian inferential methods for. Bibliographic notes the potential outcomes model for causal inference was first advocated by neyman 1923 and rubin 1974. The perspective on causal inference taken in this course is often referred to as the rubin causal model e. Bayesian inference for causal effects in randomized. Causal validity considerations for including high quality non.

Topical outlinereading for lectures ir imbens rubin. The fundamental problem of causal inference holland, 1986, jasa i for each unit, we can observe at most one of the two potential outcomes, the other is missing counterfactual. A common mindset is that causal inference is only possible using randomised experiments, but developments in statistics and related elds have shown that this view is oversimpli ed and restrictive. In section 3 we develop an alternative approach based on the rcm, and the approaches are contrasted in section 4. Causal inference in statistics, social, and biomedical sciences and. A large literature on causal inference in statistics, econometrics, biostatistics, and epidemiology see, e. Pdf causal inference for statistics, social, and biomedical. Jul 16, 2017 growing literature on causal inference in the social sciences pearl, 2009. Rejoinder on causal inference through potential outcomes and principal stratification. Identification of causal effects us ing instrumental variables. Three primary features distinguish the rubin causal model. Two predominant causal inference frameworks are structural causal models scm pearl and potential outcomes rubin 1974, 2005, which are complementary and theoretically connected pearl.