Ivan A. Canay
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View article: Testing Conditional Stochastic Dominance at Target Points
Testing Conditional Stochastic Dominance at Target Points Open
This paper introduces a novel test for conditional stochastic dominance (CSD) at specific values of the conditioning covariates, referred to as target points. The test is relevant for analyzing income inequality, evaluating treatment effec…
View article: Decomposition and Interpretation of Treatment Effects in Settings with Delayed Outcomes
Decomposition and Interpretation of Treatment Effects in Settings with Delayed Outcomes Open
This paper studies settings where the analyst is interested in identifying and estimating the average \emph{direct} causal effect of a binary treatment on an outcome. We consider a setup in which the outcome realization does not get immedi…
View article: Inference for Cluster Randomized Experiments with Non-ignorable Cluster Sizes
Inference for Cluster Randomized Experiments with Non-ignorable Cluster Sizes Open
This paper considers the problem of inference in cluster randomized experiments when cluster sizes are non-ignorable. Here, by a cluster randomized experiment, we mean one in which treatment is assigned at the cluster level. By non-ignorab…
View article: A User's Guide to Approximate Randomization Tests with a Small Number of Clusters
A User's Guide to Approximate Randomization Tests with a Small Number of Clusters Open
This paper provides a user's guide to the general theory of approximate randomization tests developed in Canay, Romano, and Shaikh (2017) when specialized to linear regressions with clustered data. Such regressions include settings in whic…
View article: On the implementation of Approximate Randomization Tests in Linear Models with a Small Number of Clusters
On the implementation of Approximate Randomization Tests in Linear Models with a Small Number of Clusters Open
This paper provides a user's guide to the general theory of approximate randomization tests developed in Canay, Romano, and Shaikh (2017) when specialized to linear regressions with clustered data. An important feature of the methodology i…
View article: On the Use of Outcome Tests for Detecting Bias in Decision Making
On the Use of Outcome Tests for Detecting Bias in Decision Making Open
The decisions of judges, lenders, journal editors, and other gatekeepers often lead to significant disparities across affected groups.An important question is whether, and to what extent, these group-level disparities are driven by relevan…
View article: Replication data for: "The Wild Bootstrap with a Small Number of Large Clusters"
Replication data for: "The Wild Bootstrap with a Small Number of Large Clusters" Open
Canay, Ivan A., Santos, Andres, and Shaikh, Azeem M., (2021) “The Wild Bootstrap with a “Small” Number of “Large” Clusters.” Review of Economics and Statistics 103:2, 346–363.
View article: temp_prov_processed.tab
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Data
View article: Inference under covariate‐adaptive randomization with multiple treatments
Inference under covariate‐adaptive randomization with multiple treatments Open
This paper studies inference in randomized controlled trials with covariate‐adaptive randomization when there are multiple treatments. More specifically, we study in this setting inference about the average effect of one or more treatments…
View article: readme.txt
readme.txt Open
README
View article: readme.txt
readme.txt Open
README
View article: Wild.Functions.R
Wild.Functions.R Open
Rfile
View article: meng-qian-yared_rep.R
meng-qian-yared_rep.R Open
Rfile
View article: temp_prov_include_auto_processed.tab
temp_prov_include_auto_processed.tab Open
Data
View article: Inference under Covariate-Adaptive Randomization with Multiple Treatments
Inference under Covariate-Adaptive Randomization with Multiple Treatments Open
This paper studies inference in randomized controlled trials with covariate-adaptive randomization when there are multiple treatments. More specifically, we study inference about the average effect of one or more treatments relative to oth…
View article: The wild bootstrap with a "small" number of "large" clusters
The wild bootstrap with a "small" number of "large" clusters Open
This paper studies the properties of the wild bootstrap-based test proposed in Cameron et al. (2008) in settings with clustered data. Cameron et al. (2008) provide simulations that suggest this test works well even in settings with as few …
View article: Testing Continuity of a Density via g-order statistics in the Regression Discontinuity Design
Testing Continuity of a Density via g-order statistics in the Regression Discontinuity Design Open
In the regression discontinuity design (RDD), it is common practice to assess the credibility of the design by testing the continuity of the density of the running variable at the cut-off, e.g., McCrary (2008). In this paper we propose an …
View article: Approximate Permutation Tests and Induced Order Statistics in the Regression Discontinuity Design
Approximate Permutation Tests and Induced Order Statistics in the Regression Discontinuity Design Open
In the regression discontinuity design (RDD), it is common practice to assess the credibility of the design by testing whether the means of baseline covariates do not change at the cut-off (or threshold) of the running variable. This pract…
View article: Inference under covariate-adaptive randomization
Inference under covariate-adaptive randomization Open
This article studies inference for the average treatment effect in randomized controlled trials with covariate-adaptive randomization. Here, by covariate-adaptive randomization, we mean randomization schemes that first stratify according t…
View article: Approximate permutation tests and induced order statistics in the regression discontinuity design
Approximate permutation tests and induced order statistics in the regression discontinuity design Open
In the regression discontinuity design (RDD), it is common practice to asses the credibility of the design by testing whether the means of baseline covariates do not change at the cutoff (or threshold) of the running variable. This practic…
View article: Inference for subvectors and other functions of partially identified parameters in moment inequality models
Inference for subvectors and other functions of partially identified parameters in moment inequality models Open
This paper introduces a bootstrap-based inference method for functions of the parameter vector in a moment (in)equality model. These functions are restricted to be linear for two-sided testing problems, but may be nonlinear for one-sided t…
View article: Randomization Tests Under an Approximate Symmetry Assumption
Randomization Tests Under an Approximate Symmetry Assumption Open
This paper develops a theory of randomization tests under an approximate symmetry assumption. Randomization tests provide a general means of constructing tests that control size in nite samples whenever the distribution of the observed dat…
View article: Inference for functions of partially identified parameters in moment inequality models
Inference for functions of partially identified parameters in moment inequality models Open
This paper introduces a bootstrap-based inference method for functions of the parameter vector in a moment (in)equality model. As a special case, our method yields marginal confidence sets for individual coordinates of this parameter vecto…