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Semiparametric Causality Tests Using the Policy Propensity Score

Angrist, Joshua D.; Kuersteiner, Guido M. - 2021

Time series data are widely used to explore causal relationships, typically in a regression framework with lagged dependent variables. Regression-based causality tests rely on an array of functional form and distributional assumptions for valid causal inference. This paper develops a...

Persistent link: https://www.econbiz.de/10013221886

Semiparametric Estimation of Instrumental Variable Models for Causal Effects

Abadie, Alberto - 2021

This article introduces a new class of instrumental variable (IV) estimators of causal treatment effects for linear and nonlinear models with covariates. The rationale for focusing on nonlinear models is to improve the approximation to the causal response function of interest. For example, if...

Persistent link: https://www.econbiz.de/10013239198

Combining Matching and Synthetic Control to Trade Off Biases from Extrapolation and Interpolation

Kellogg, Maxwell - 2020

matching estimators exhibit the opposite behavior: they limit interpolation bias at the potential expense of extrapolation bias …. We propose combining the matching and synthetic control estimators through model averaging to create an estimator called … the SC, matching, MASC and penalized SC estimators do (and do not) perform well. Then, we use the MASC re-examine the …

Persistent link: https://www.econbiz.de/10012844744

On the Failure of the Bootstrap for Matching Estimators

Abadie, Alberto - 2010

Matching estimators are widely used for the evaluation of programs or treatments. Often researchers use bootstrapping … neighbor matching, the standard conditions for the bootstrap are not satisfied, leading the bootstrap variance to diverge from …

Persistent link: https://www.econbiz.de/10012761283

Using Matching, Instrumental Variables and Control Functions to Estimate Economic Choice Models

Heckman, James J.; Navarro, Salvador - 2022

selection) in matching, instrumental variable and control functions methods. (2) It contrasts the roles of exclusion … restrictions in matching and selection models. (3) It characterizes the sensitivity of matching to the choice of conditioning …. (4) It demonstrates the problem of choosing the conditioning variables in matching and the failure of conventional model …

Persistent link: https://www.econbiz.de/10013311183

Matching Methods in Practice : Three Examples

Imbens, Guido - 2014

selection--on--observables type assumptions using matching or propensity score methods. Much of this literature is highly …

Persistent link: https://www.econbiz.de/10013057405

Panel Data Estimators for Nonseparable Models with Endogenous Regressors

Altonji, Joseph G.; Matzkin, Rosa L. - 2021

We propose two new estimators for a wide class of panel data models with nonseparable error terms and endogenous explanatory variables. The first estimator covers qualitative choice models and both estimators cover models with continuous dependent variables. The first estimator requires the...

Persistent link: https://www.econbiz.de/10013237043

Design-Based Analysis in Difference-in-Differences Settings with Staggered Adoption

Athey, Susan - 2018

In this paper we study estimation of and inference for average treatment effects in a setting with panel data. We focus on the setting where units, e.g., individuals, firms, or states, adopt the policy or treatment of interest at a particular point in time, and then remain exposed to this...

Persistent link: https://www.econbiz.de/10012911687

Pre-Event Trends in the Panel Event-Study Design

Freyaldenhoven, Simon - 2018

We consider a linear panel event-study design in which unobserved confounds may be related both to the outcome and to the policy variable of interest. We provide sufficient conditions to identify the causal effect of the policy by exploiting covariates related to the policy only through the...

Persistent link: https://www.econbiz.de/10012920350

Matrix Completion Methods for Causal Panel Data Models

Athey, Susan - 2018

In this paper we study methods for estimating causal effects in settings with panel data, where a subset of units are exposed to a treatment during a subset of periods, and the goal is estimating counterfactual (untreated) outcomes for the treated unit/period combinations. We develop a class of...

Persistent link: https://www.econbiz.de/10012909860