What warrants a causal inference, as opposed to a descriptive regularity. Counterfactuals and the future of empirical research in observational social science. In this second edition of counterfactuals and causal inference, completely revised and expanded, the essential features. The importance of causal effect heterogeneity is stressed throughout the book, and the need fordeepcausal explanationvia mechanisms is discussed. Vermeersch impact evaluation in practice, second edition published. Morgan and winship, counterfactuals and causal inference, 2nd ed. As a result, large segments of the statistical research community. Inferring causality through counterfactuals in observational studies. Causal inference based on counterfactuals bmc medical. Wewill discuss the broader philosophical literature in chapters 8 and 10, as it does have some implications. Methods and principles for social research by stephen l.
Social scien tists could now calculate almost everything with little manual labor. Causal inference is the process by which one can use data to make claims about causal relationships. Morgan and christopher winship, available from the library of congress. In this second edition of counterfactuals and causal inference, completely revised and expanded, the essential features of the counterfactual approach to observational data analysis are presented with examples from the social, demographic, and health sciences. Homework 40%, midterm 30%, final exam 30% prerequisites math. Cambridge core statistical theory and methods counterfactuals and causal inference by stephen l.
Use features like bookmarks, note taking and highlighting while reading counterfactuals and causal inference. The above quotation is key to understanding the problems of selection and endogeneity. Counterfactual causality and empirical research in. These are very demanding assumptions for an observational study. In this section, we present a selective history of the literature on these challenges, focusing on the varied history of the usage of experimental. Causal inference principle i suppose we wish to estimate the causal effect of a on y. An introduction to causal inference judea pearl abstract this paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data.
The paper concludes by urging greater appreciation of the differences between the statistical and settheoretic approaches to causal analysis. Causal inference and counterfactuals authorseditors. It focuses on one of the most active areas of recent research. Counterfactuals much recent analysis of causal inference is grounded in the manipulation of counterfactuals. Philosophically, a counterfactual statement is an assertion of the form if x had been the case, then y would have happened, made when it is known to be false that x is the case. Handbook of causal analysis for social research handbook. Review of counterfactuals and causal inference 4 chose, then an average difference in the values of can be given a causal interpretation morgan and winship, 2007, p. This paper contributes to the debate on the virtues and vices of counterfactuals as a basis for causal inference.
By contrast, settheoretic researchers use counterfactuals to estimate models by making explicit their assumptions about empty sectors in the vector space defined by the causal variables. See also the online appendix in pdf or word with stata code for the analysis. A missing data perspective peng ding fan li 1 abstract inferring causal effects of treatments is a central goal in many disciplines. As pearl documents comprehensively with a wide range of examples, this. Section 7 questions the role of an implicit attitude of fatalism in some counterfactual causal models and methods. For example, for the causal effect of having a college degree. Morgan is the bloomberg distinguished professor of sociology and education at johns hopkinsuniversity. Introduction to causal inference without counterfactuals. Dawid jasa, 2000, see also his notes for a course on casual inference at.
Philosophers and logicians define counterfactuals as subjunctive conditional. Counterfactuals university of california, berkeley. Models and principles for social research stephen morgan, christopher winship download bok. Jan 01, 2007 counterfactuals and causal inference book. Stephen morgan has been published in top tier journals for sociological research and has been cited more than 4,000 times in the academic literature.
Download for offline reading, highlight, bookmark or take notes while you read counterfactuals and causal inference. For a recent collection of essays in philosophy on counterfactuals and causation, see collins, hall, and paul 2004. How do people understand and reason from conditionals. Methods and principles for social science research download download pdf. Causal inference in the social sciences university. Wewill discuss the broader philosophical literature in chapters 8 and 10, as it does have some implications for social science practice and the pursuit of explanation more generally.
Alternative estimation techniques are first introduced using both the potential outcome model and causal graphs. Morgan editor handbook of causal analysis for social research what constitutes a causal explanation, and must an explanation be causal. If c is a common cause of a and y then we should control for c c a y if we do not control for c, then the association we observe between a and y may not be due to the causal effect of a on y. Morgan is associate professor of sociology and the director of the. Morgan and christopher winships 2007 book counterfactual and causal inference along with shadish, cook, and campbell s experimental and quasiexperimental designs for generalized. A critique of counterfactuals in causal inference is in. Methods and principles for social research analytical methods for social research. These two files are also supposed to be available on the sociological methods and. The use of counterfactuals for causal inference has brought clarity to our reasoning about causality. Counterfactual theories of causation stanford encyclopedia. These counterfactuals are of the form if a were the case pr c would be x, where the counterfactual is an ordinary wouldcounterfactual, interpreted according to the semantics above, and the pr operator is a probability operator with narrow scope confined to the consequent of the counterfactual. Causal inference topics brandon stewart1 princeton april 10 april 19, 2017 1this lecture draws from slides by matt blackwell, jens hainmueller, erin hartman and gary king stewart princeton causal inference apr 10 apr 19, 2017 1 168.
These include causal interactions, imperfect experiments, adjustment for. Handbooks of sociology and social research handbook of causal analysis for social research stephen l. Morgan skip to main content accessibility help we use cookies to distinguish you from other users and to provide you with a better experience on our websites. Additional readings will be made available on the course website as needed.
The essential features of the counterfactual model of causality for observational data analysis are presented with. Gertler, sebastian martinez, patrick premand, laura b. Some experience with regression analysis will be useful. Return to article details counterfactual and causal inference. Morgan a nd winship, counterfactuals and causal inference, 2nd ed. Back and front door partial compliance and instrumental variables. Adam kelleher syllabus and tentative schedule general information meeting time. No book can possibly provide a comprehensive description of methodologies for causal inference across the sciences. A cause, according to lewis in his 1973 article causation, was something that. The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies. This book presents a model and set of methods for causal effect estimation that social scientists can use to address causal questions such as these. Counterfactuals confounding criteria counterfactual causality by making such assumptions by looking at the ate we are making a counterfactual argument. Its aim is to present a survey of some recent research in causal inference. Misunderstandings among experimentalists and observationalists about causal inference.
Mar 27, 20 by contrast, settheoretic researchers use counterfactuals to estimate models by making explicit their assumptions about empty sectors in the vector space defined by the causal variables. All content included on our site, such as text, images, digital downloads and other, is the property of its content suppliers and protected by us and international laws. Introduction to causal inference without counterfactuals a. Introduction inferences about counterfactuals are essential for prediction, answering what if questions, and estimating causal e.
Inferring causality through counterfactuals in observational. Download it once and read it on your kindle device, pc, phones or tablets. Read counterfactuals and causal inference methods and principles for social research by stephen l. We are making assumptions of what y 1i would have been, had i had a college degree. Counterfactuals and causal inference stephen l morgan. We discuss a number of issues, ranging from its nonobservable basis to the. Table of contents for counterfactuals and causal inference. Counterfactuals and causal inference ebook by stephen l. It is an excellent introduction to the topic, and a fine place to begin learning causal inference. You could put all your eggs on the propensityscore basket and hope for the best but i would advise it. The graphical approach to causal inference using directed acyclic graphs dags is equivalent to the potential outcomes approach to causal inference.
General interest counterfactuals and causal inference by stephen l. Methods and principles for social research ebook written by stephen l. An introduction to causal inference, with extensions to. Since inferring causal relationships is one of the central tasks of science, it is a topic that has. Nonparametric preprocessing for reducing model dependence in parametric causal inference, political analysis, 15 2007. Analytical methods for social research revised edition of the authors counterfactuals and causal inference, published in 2007. May 20 this workshop focuses on graphical causal models. Special emphasis is placed on the assumptions that underlie all causal.
The essential features of the counterfactual model of causality for observational data analysis are presented with examples from sociology, political science, and economics. The goal is to put the counterfactual approach in an epistemological perspective. These models are based on ideas rst broached by statisticians in the early twentieth. Pearlcausal inference in statistics 98 in the standard mathematicallanguageof statistics, and these extensions are not generally emphasized in the mainstream literature and education.