Guido W. Imbens

Guido W. Imbens
Stanford University | SU

About

20
Publications
11,910
Reads
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11,848
Citations
Citations since 2016
6 Research Items
8010 Citations
201620172018201920202021202202004006008001,0001,2001,400
201620172018201920202021202202004006008001,0001,2001,400
201620172018201920202021202202004006008001,0001,2001,400
201620172018201920202021202202004006008001,0001,2001,400

Publications

Publications (20)
Article
It is common in regression discontinuity analysis to control for third, fourth, or higher-degree polynomials of the forcing variable. There appears to be a perception that such methods are theoretically justified, even though they can lead to evidently nonsensical results. We argue that controlling for global high-order polynomials in regression di...
Article
In this paper we propose methods for estimating heterogeneity in causal effects in experimental and observational studies and for conducting hypothesis tests about the magnitude of differences in treatment effects across subsets of the population. We provide a data-driven approach to partition the data into subpopulations that differ in the magnitu...
Article
In this review, we present econometric and statistical methods for analyzing randomized experiments. For basic experiments we stress randomization-based inference as opposed to sampling-based inference. In randomization-based inference, uncertainty in estimates arises naturally from the random assignment of the treatments, rather than from hypothes...
Article
Full-text available
In this paper we discuss recent developments in econometrics that we view as important for empirical researchers working on policy evaluation questions. We focus on three main areas, where in each case we highlight recommendations for applied work. First, we discuss new research on identification strategies in program evaluation, with particular fo...
Article
Full-text available
Estimating the long-term effects of treatments is of interest in many fields. A common challenge in estimating such treatment effects is that long-term outcomes are unobserved in the time frame needed to make policy decisions. One approach to overcome this missing data problem is to analyze treatments effects on an intermediate outcome, often calle...
Article
In this paper, we develop new methods for estimating average treatment effects in observational studies, focusing on settings with more than two treatment levels under unconfoundedness given pre-treatment variables. We emphasize subclassification and matching methods which have been found to be effective in the binary treatment literature and which...
Article
Full-text available
There is a large theoretical literature on methods for estimating causal effects under unconfoundedness, exogeneity, or selection- on- observables type assumptions using matching or propensity score methods. Much of this literature is highly technical and has not made inroads into empirical practice where many researchers continue to use simple met...
Book
Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding...
Article
I review recent work in the statistics literature on instrumental variables methods from an econometrics perspective. I discuss some of the older, economic, applications including supply and demand models and relate them to the recent applications in settings of randomized experiments with noncompliance. I discuss the assumptions underlying instrum...
Article
Full-text available
There is growing appreciation for the advantages of experimentation in the social sciences. Policy-relevant claims that in the past were backed by theoretical arguments and inconclusive correlations are now being investigated using more credible methods. Changes have been particularly pronounced in development economics, where hundreds of randomize...
Article
The existing literature on the effect of the timing of first birth on women's wages generally concludes that there is a benefit to fertility delay, but one that is overstated by the raw correlation. In this paper I reconsider this question, but begin by diverging from the literature to redefine "timing" in terms of a woman's entry into the labor fo...
Article
Full-text available
The purpose of this paper is to estimate sharp bounds on treatment effects of education programs that ration excess demand by admission lotteries when selective attrition cannot be ignored. Differential attrition arises in these models because students that lose the lottery are more likely to pursue educational options outside the school district....
Article
How can price elasticities be identified when agents face optimization frictions such as adjustment costs or inattention? I derive bounds on structural price elasticities that are a function of the observed effect of a price change on demand, the size of the price change, and the degree of frictions. The degree of frictions is measured by the utili...
Article
Since George A. Akerlof (1970), economists have understood the adverse selection problem that information asymmetries can create in used goods mar-kets. The remarkable growth in online used goods auctions thus poses a puzzle. Part of the solution is that sellers voluntarily disclose their private information on the auction webpage. This defines a p...
Article
Full-text available
I estimate the impacts of secondary school on human capital, occupational choice, and fertility for young adults in Kenya. Probability of admission to government secondary school rises sharply at a score close to the national mean on a standardized 8th grade examination, permitting me to estimate causal effects of schooling in a regression dis-cont...
Article
For most of this century, randomization has been a cornerstone of scientific experimentation, especially when dealing with humans as experimental units. In practice, however, noncompliance is relatively common with human subjects, complicating traditional theories of inference that require adherence to the random treatment assignment. In this paper...
Article
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. To address the problems associated with comparing subjects by the ignorable assignment - an "intention-to-treat analysis" - we make use of in...

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