Výsledky vyhledávání

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Decomposition and Interpretation of Treatment Effects in Settings with Delayed Outcomes

Autor: Bugni, Federico A., Canay, Ivan A., McBride, Steve

This paper studies settings where the analyst is interested in identifying and estimating the average causal effect of a binary treatment on an outcome. We consider a setup in which the outcome realization does not get immediately realized after the

Externí odkaz: http://arxiv.org/abs/2302.11505

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Inference for Cluster Randomized Experiments with Non-ignorable Cluster Sizes

Autor: Bugni, Federico, Canay, Ivan, Shaikh, Azeem, Tabord-Meehan, Max

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-ignorable cluster

Externí odkaz: http://arxiv.org/abs/2204.08356

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On the implementation of Approximate Randomization Tests in Linear Models with a Small Number of Clusters

Autor: Cai, Yong, Canay, Ivan A., Kim, Deborah, Shaikh, Azeem M.

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 is that it a

Externí odkaz: http://arxiv.org/abs/2102.09058

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Akademický článek

A User’s guide for inference in models defined by moment inequalities

Autor: Canay, Ivan A., Illanes, Gastón, Velez, Amilcar

Publikováno v: In Journal of Econometrics September 2023

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Inference under Covariate-Adaptive Randomization with Multiple Treatments

Autor: Bugni, Federico A., Canay, Ivan A., Shaikh, Azeem M.

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 other treatmen

Externí odkaz: http://arxiv.org/abs/1806.04206

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Testing Continuity of a Density via g-order statistics in the Regression Discontinuity Design

Autor: Bugni, Federico A., Canay, Ivan A.

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 approximate

Externí odkaz: http://arxiv.org/abs/1803.07951

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