Avoiding Bad Control in Regression for Partially Qualitative Outcomes, and Correcting for Endogeneity Bias in Two-Part Models: Causal Inference from the Potential Outcomes Perspective

Indiana University-Purdue University Indianapolis (IUPUI) === The general potential outcomes framework (GPOF) is an essential structure that facilitates clear and coherent specification, identification, and estimation of causal effects. This dissertation utilizes and extends the GPOF, to specify, id...

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Main Author: Asfaw, Daniel Abebe
Other Authors: Terza, Joseph
Language:en_US
Published: 2021
Subjects:
BMI
Online Access:http://hdl.handle.net/1805/25997
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spelling ndltd-IUPUI-oai-scholarworks.iupui.edu-1805-259972021-05-26T05:06:49Z Avoiding Bad Control in Regression for Partially Qualitative Outcomes, and Correcting for Endogeneity Bias in Two-Part Models: Causal Inference from the Potential Outcomes Perspective Asfaw, Daniel Abebe Terza, Joseph Ottoni-Wilhelm, Mark Tennekoon, Vidhura Tan, Fei Bad Control BMI Endogeneity Generalized Control Function Medical Spending Two-Part Model Indiana University-Purdue University Indianapolis (IUPUI) The general potential outcomes framework (GPOF) is an essential structure that facilitates clear and coherent specification, identification, and estimation of causal effects. This dissertation utilizes and extends the GPOF, to specify, identify, and estimate causally interpretable (CI) effect parameter (EP) for an outcome of interest that manifests as either a value in a specified subset of the real line or a qualitative event -- a partially qualitative outcome (PQO). The limitations of the conventional GPOF for casting a regression model for a PQO is discussed. The GPOF is only capable of delivering an EP that is subject to a bias due to bad control. The dissertation proposes an outcome measure that maintains all of the essential features of a PQO that is entirely real-valued and is not subject to the bad control critique; the P-weighted outcome – the outcome weighted by the probability that it manifests as a quantitative (real) value. I detail a regression-based estimation method for such EP and, using simulated data, demonstrate its implementation and validate its consistency for the targeted EP. The practicality of the proposed approach is demonstrated by estimating the causal effect of a fully effective policy that bans pregnant women from smoking during pregnancy on a new measure of birth weight. The dissertation also proposes a Generalized Control Function (GCF) approach for modeling and estimating a CI parameter in the context of a fully parametric two-part model (2PM) for a continuous outcome in which the causal variable of interest is continuous and endogenous. The proposed approach is cast within the GPOF. Given a fully parametric specification for the causal variable and under regular Instrumental Variables (IV) assumptions, the approach is shown to satisfy the conditional independence assumption that is often difficult to hold under alternative approaches. Using simulated data, a full information maximum likelihood (FIML) estimator is derived for estimating the “deep” parameters of the model. The Average Incremental Effect (AIE) estimator based on these deep parameter estimates is shown to outperform other conventional estimators. I apply the method for estimating the medical care cost of obesity in youth in the US. 2021-05-24T17:49:32Z 2021-05-24T17:49:32Z 2021-05 Dissertation http://hdl.handle.net/1805/25997 en_US
collection NDLTD
language en_US
sources NDLTD
topic Bad Control
BMI
Endogeneity
Generalized Control Function
Medical Spending
Two-Part Model
spellingShingle Bad Control
BMI
Endogeneity
Generalized Control Function
Medical Spending
Two-Part Model
Asfaw, Daniel Abebe
Avoiding Bad Control in Regression for Partially Qualitative Outcomes, and Correcting for Endogeneity Bias in Two-Part Models: Causal Inference from the Potential Outcomes Perspective
description Indiana University-Purdue University Indianapolis (IUPUI) === The general potential outcomes framework (GPOF) is an essential structure that facilitates clear and coherent specification, identification, and estimation of causal effects. This dissertation utilizes and extends the GPOF, to specify, identify, and estimate causally interpretable (CI) effect parameter (EP) for an outcome of interest that manifests as either a value in a specified subset of the real line or a qualitative event -- a partially qualitative outcome (PQO). The limitations of the conventional GPOF for casting a regression model for a PQO is discussed. The GPOF is only capable of delivering an EP that is subject to a bias due to bad control. The dissertation proposes an outcome measure that maintains all of the essential features of a PQO that is entirely real-valued and is not subject to the bad control critique; the P-weighted outcome – the outcome weighted by the probability that it manifests as a quantitative (real) value. I detail a regression-based estimation method for such EP and, using simulated data, demonstrate its implementation and validate its consistency for the targeted EP. The practicality of the proposed approach is demonstrated by estimating the causal effect of a fully effective policy that bans pregnant women from smoking during pregnancy on a new measure of birth weight. The dissertation also proposes a Generalized Control Function (GCF) approach for modeling and estimating a CI parameter in the context of a fully parametric two-part model (2PM) for a continuous outcome in which the causal variable of interest is continuous and endogenous. The proposed approach is cast within the GPOF. Given a fully parametric specification for the causal variable and under regular Instrumental Variables (IV) assumptions, the approach is shown to satisfy the conditional independence assumption that is often difficult to hold under alternative approaches. Using simulated data, a full information maximum likelihood (FIML) estimator is derived for estimating the “deep” parameters of the model. The Average Incremental Effect (AIE) estimator based on these deep parameter estimates is shown to outperform other conventional estimators. I apply the method for estimating the medical care cost of obesity in youth in the US.
author2 Terza, Joseph
author_facet Terza, Joseph
Asfaw, Daniel Abebe
author Asfaw, Daniel Abebe
author_sort Asfaw, Daniel Abebe
title Avoiding Bad Control in Regression for Partially Qualitative Outcomes, and Correcting for Endogeneity Bias in Two-Part Models: Causal Inference from the Potential Outcomes Perspective
title_short Avoiding Bad Control in Regression for Partially Qualitative Outcomes, and Correcting for Endogeneity Bias in Two-Part Models: Causal Inference from the Potential Outcomes Perspective
title_full Avoiding Bad Control in Regression for Partially Qualitative Outcomes, and Correcting for Endogeneity Bias in Two-Part Models: Causal Inference from the Potential Outcomes Perspective
title_fullStr Avoiding Bad Control in Regression for Partially Qualitative Outcomes, and Correcting for Endogeneity Bias in Two-Part Models: Causal Inference from the Potential Outcomes Perspective
title_full_unstemmed Avoiding Bad Control in Regression for Partially Qualitative Outcomes, and Correcting for Endogeneity Bias in Two-Part Models: Causal Inference from the Potential Outcomes Perspective
title_sort avoiding bad control in regression for partially qualitative outcomes, and correcting for endogeneity bias in two-part models: causal inference from the potential outcomes perspective
publishDate 2021
url http://hdl.handle.net/1805/25997
work_keys_str_mv AT asfawdanielabebe avoidingbadcontrolinregressionforpartiallyqualitativeoutcomesandcorrectingforendogeneitybiasintwopartmodelscausalinferencefromthepotentialoutcomesperspective
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