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Identifying Effects of Multivalued Treatments

Sokbae Lee, Bernard Salanié

arXiv 30 Apr 2018 · Econometrics · publishedEconometrica (2018) · 64 citations (OpenAlex)

arXiv:1805.00057 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Multivalued treatment models have typically been studied under restrictive assumptions: ordered choice, and more recently unordered monotonicity. We show how treatment effects can be identified in a more general class of models that allows for multidimensional unobserved heterogeneity. Our results rely on two main assumptions: treatment assignment must be a measurable function of threshold-crossing rules, and enough continuous instruments must be available. We illustrate our approach for several classes of models.

Citation extraction

42
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88
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distinct cited
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12,639
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appendix boundary found by appendix_command · 66% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Heckman and Pinto (2018) Unordered Monotonicity0.9619489%
2Matzkin (1993) Nonparametric identification and estimation of polychotomous choice models0.9285480%
3Matzkin (2007) Heterogeneous choice0.9285480%
4Heckman, Urzua, and Vytlacil (2008) Instrumental variables in models with multiple outcomes: The general unordered case0.79410450%
5Heckman and Vytlacil (2005) Structural Equations, Treatment Effects, and Econometric Policy Evaluation0.73732100%
6Angrist and Imbens (1995) Two-stage least squares estimation of average causal effects in models with variable treatment intensity0.73732100%
7Gautier and Hoderlein (2015) A Triangular Treatment Effect Model With Random Coefficients in the Selection Equation0.73732100%
8Chesher (2003) Identification in Nonseparable Models0.64422100%
9D'Haultf“uille and Février (2015) Identification of Nonseparable Triangular Models With Discrete Instruments0.64422100%
10Florens, Heckman, Meghir, and Vytlacil (2008) Identification of Treatment Effects Using Control Functions in Models With Continuous, Endogenous Treatment and Heterogeneous Ef…0.64422100%

Showing the top 10 of 42 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Identification in Multiple Treatment Models under Discrete Variation1.00083
2Treatment Effects of Multi-Valued Treatments in Hyper-Rectangle Model1.00084
3Treatment Effect Models with Strategic Interaction in Treatment Decisions1.00073
4Treatment Effects with Multidimensional Unobserved Heterogeneity: Identification of the Marginal Treatment Effect Toshiki Tsuda Yale University0.959175
52SLS with Multiple Treatments0.92843
6Multiple Treatments with Strategic Interaction0.81142
7Policy Relevant Treatment Effects with Multidimensional Unobserved Heterogeneity0.73732
8Treatment Effects with Targeting Instruments0.64422
9Contamination Bias in Linear Regressions0.64422
10Testing Exclusion and Shape Restrictions in Potential Outcomes Models0.64422