Bounding average treatment effects using linear programming

Lukáš Lafférs

This paper presents a method of calculating sharp bounds on the average treatment effect using linear programming under identifying assumptions commonly used in the literature. This new method provides a sensitivity analysis of the identifying assumptions and missing data in an application regarding the effect of parent's schooling on children's schooling. Even a mild departure from identifying assumptions may substantially widen the bounds on average treatment effects. Allowing for a small fraction of the data to be missing also has a large impact on the results.

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Year of publication:	2015
Authors:	Lafférs, Lukáš
Publisher:	London : Centre for Microdata Methods and Practice
Subject:	Partial identification \| Bounds \| Average treatment effect \| Sensitivity analysis \| Kausalanalyse \| Causality analysis \| Mathematische Optimierung \| Mathematical programming \| Sensitivitätsanalyse \| Schätztheorie \| Estimation theory \| IV-Schätzung \| Instrumental variables \| Nichtparametrisches Verfahren \| Nonparametric statistics

Extent:	Online-Ressource (37 S.) graph. Darst.
Series:	CEMMAP working papers / Centre for Microdata Methods and Practice. - London : [Verlag nicht ermittelbar], ISSN 1753-9196, ZDB-ID 2106928-1. - Vol. 70/15
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Arbeitspapier ; Working Paper ; Graue Literatur ; Non-commercial literature
Language:	English
Other identifiers:	10.1920/wp.cem.2015.7015 [DOI] hdl:10419/130074 [Handle]
Classification:	C4 - Econometric and Statistical Methods: Special Topics ; C6 - Mathematical Methods and Programming ; I2 - Education
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://ebvufind01.dmz1.zbw.eu/10011380632