Angrist also developed with imbens and krueger socalled jackknife instrumental variables estimators to address the bias in 2sls estimates in overidentified models and has explored the interpretation of iv estimators in simultaneous equations models along with imbens and kathryn graddy. Causal inference in statistics, social, and biomedical sciences. This book, at once transparent and deep, will be both a fantastic introduction to fundamental principles and a practical resource for students and practitioners. It is an introduction in the sense that it is 600 pages and still doesnt have room for differenceindifferences, regression discontinuity, synthetic controls, power calculations, dealing with attrition. Identification and estimation of local average treatment effects. The books listed below are available at various online bookstores. Cattaneo, journal of the american statistical association guido imbens and donald rubin have written an authoritative textbook on causal inference that is expected to have a lasting impact on social and biomedical scientists as well as statisticians. There are various ways of expressing such assumptions, and these are talked about in various ways in your books, in the books by angrist and pischke, in the book by imbens. The rubin model is a version of the widely used econometric switching regression model maddalla 1983. Identification of causal effects using instrumental variables. Mostly harmless econometrics princeton university press.
Sep 07, 2015 comments on table of contents and the 5 sample chapters of causal inference in statistics, by rubin and imbens. Nber 20 method lectures, econometric methods for highdimensional data chernozhukov, gentzkow, hansen, shapiro, taddy. Beyond this, you could read the books by morgan and winship and pearl, but both these are a bit more technical and less applied that the two books linked to above. Instrumental variables estimates of the effect of subsidized training on the quantiles of trainee earnings, econometrica, econometric society, vol. For example, in their 1996 article, angrist, imbens, and rubin showed how, under certain. In this groundbreaking text, two worldrenowned experts present statistical methods for. Imbens and rubin are of course wellknown developers of a lot of the theoretical literature used widely on causal analysis, and clear masters of the subject matter. After graduating from brown university guido taught at harvard university, ucla, and uc berkeley. In another recent paper angrist, imbens, and rubin 1995.
Rubin also has an excellent track record, both as a researcher and as a book author. While this is not at all light reading, it is undoubtedly good for you, and i am sure most of our readers will find they understand the subject matter better after reading this, and. Journal of the american statistical association, 1996, 91, 465468. Nber 2015 method lectures, lectures on machine learning athey and imbens. Predictive modeling, causal inference, and imbensrubin. Sources of identifying information in evaluation models, discussion paper 199142, tilburg university, center for economic. There are various ways of expressing such assumptions, and these are talked about in various ways in your books, in the books by angrist and pischke, in the book by imbens and rubin, in my book with hill, and in many places. Rubin most questions in social and biomedical sciences are causal in nature. Use features like bookmarks, note taking and highlighting while reading causal inference for statistics, social, and biomedical sciences. Identification and estimation of local average treatment effects joshua d.
Instrumental variables and the search for identification. Does maltreatment in childhood affect sexual orientation. Comments on imbens and rubin causal inference book. Social, and biomedical sciences, by imbens and rubin. Imbens and rubin come from social science and econometrics. What is the best textbook for learning causal inference. Identification and estimation of local average treatment effects guido w.
Working paper 1545, harvard institute of economic research. A fourth approach applies to settings where, in its pure form, overlap is completely absent because the assignment is a deterministic function of covariates, but comparisons can be made exploiting continuity of average outcomes as a function of. The rubin model shares many features in common with the roy model heckman and honore, 1990, roy 1951 and the model of competing risks cox, 1962. Sources of identifying information in evaluation models, nber technical working papers 0117, national bureau of economic research, inc.
Causal analysis in theory and practice 2019 january. Mit department of economics the morris and sophie chang building 50. Predictive modeling, causal inference, and imbensrubin among others when most people including me say predictive modeling, they mean noncausal predictive modeling, i. Any of the books listed below would be a good reference for the course. Aea 2018 continuing education, machine learning and econometrics athey and imbens. Discount prices on books by jordan rubin, including titles like patient heal thyself. For example, in their 1996 article, angrist, imbens, and rubin showed how, under certain assumptions, conditioning on an intermediate outcome leads to an inference that is similar to an instrumental variables estimate.
These books are not required, but most purchase them. More recently, methods also became available to address treatment noncompliance using potential outcomes, starting mainly with work by baker and lindeman 1994, imbens and rubin 1994, robins and greenland 1994, angrist, imbens, and rubin 1996, and are currently receiving even more attention e. An introduction 9780521885881 by imbens, guido w rubin, donald b. By putting the potential outcome framework at the center of our understanding of causality, imbens and rubin have ushered in a fundamental transformation of empirical work in economics. Machine learning and prediction in economics and finance. Apr 06, 2015 causal inference for statistics, social, and biomedical sciences. Quantitative empirical methods reading list department of. Mark mcclellan, director of the health care innovation and value initiative, brookings institution, washington dc this book will revolutionize how applied statistics is taught in. Whats new in econometrics, national bureau of economic research, cambridge, ma, july 30 august 1, 2007. Many chapters will be assigned as supplemental reading.
Estimating outcome distributions for compliers in instrumental variables models. The statistics of causal inference in the social sciences. Stephen blyth, managing director, head of public markets, harvard management company a masterful account of the potential outcomes approach to causal inference from observational studies that rubin has been developing since he. In this groundbreaking text, two worldrenowned experts present statistical methods for studying such questions. Journal of the american statistical association 90, no. In imbens and ingrist 1994, angrist, imbens and rubin 1996 and imbens and rubin 1997,assumptions have been outlined under which instrumental variables estimands can be given a causal interpretation as a local average treatment effect without requiring functional form or constant treatment effect assumptions. Guido imbens and don rubin recently came out with a book on causal inference. Causal inference in statistics, social, and biomedical. Testing statistical hypotheses, by lehmann and romano. Joshua david angrist born in columbus, ohio on september 18, 1960 is an israeli american economist and ford professor of economics at the massachusetts institute. They are listed roughly by their relative position in the applied to theoretical continuum. Buy causal inference in statistics, social, and biomedical sciences by guido w. Recent developments in the econometrics of program. Efficient inference of average treatment effects in high dimensions via approximate residual balancing, research papers 3408, stanford university, graduate school of business.
Causal inference for statistics, social, and biomedical sciences. Guido imbens and donald rubin, causal inference for statistics, social and biomedical sciences. Stephen blyth managing director, head of public markets, harvard management company a masterful account of the potential outcomes approach to causal inference from observational studies that rubin has been developing since he. Too many books on statistical methods present a menagerie of disconnected methods and. Guido imbens is the applied econometrics professor and professor of economics at the stanford graduate school of business. Everyday low prices and free delivery on eligible orders. Twostage least squares estimation of average causal effects in models with variable treatment intensity.
Over the summer ive been slowly working my way through the new book causal inference for statistics, social, and biomedical sciences. We investigate conditions sufficient for identification of average treatment effects using instrumental variables. A fourth approach applies to settings where, in its pure form, overlap is completely absent because the assignment is a deterministic function of covari. Mm denotes chapters from angristpischkes mastering metrics. Quantitative empirical methods reading list department.
Joshua angrist and jornsteffan pischke, mostly harmless econometrics. Causal inference for statistics, social, and biomedical. Mit department of economics the morris and sophie chang building 50 memorial drive building e52, room 436 cambridge, ma 02142. Twostage least squares estimation of average causal.
Sep 21, 2015 imbens and rubin are of course wellknown developers of a lot of the theoretical literature used widely on causal analysis, and clear masters of the subject matter. 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. Mark mcclellan, director of the health care innovation and value initiative, brookings institution, washington dc. But none of this legitimately gives us a causal interpretation until we make some assumptions. Identification and estimation of local average treatment. Imbens and rubin provide unprecedented guidance for designing research on causal relationships, and for interpreting the results of that research appropriately. The books great of course i would say that, as ive collaborated with both authors and its so popular that i keep having to get new copies because people keep borrowing my copy and not returning it. Some comments on deaton 2009 and heckman and urzua 2009.
Theory of point estimation, by lehmann and casella. 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. This book will revolutionize how applied statistics is taught in statistics and the social and biomedical sciences. Imbens and rubin provide a rigorous foundation allowing practitioners to learn from the pioneers in the field. A fourth approach applies to settings where, in its pure form, overlap is completely absent because the assignment is a deterministic function of covariates, but comparisons can be made exploiting conti nuity of average outcomes as a function of. First off, rubin and imbens are the leaders in the field of causal inference. The interpretation of instrumental variables estimators in simultaneous equations models with an application to the demand for fish. Recent developments in the econometrics of program evaluation. Estimating treatment effects using multiple surrogates. Readings nonlinear econometric analysis economics mit. Reemployment probabilities over the business cycle, portuguese economic journal, springer, vol. Researchers have been waiting for the publication of this book. Mostly harmless econometrics, by angrist and pischke.
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