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Semiparametric Estimation of Treatment Effects in Randomized Experiments

Athey, Susan; Bickel, Peter J.; Chen, Aiyou; Imbens, Guido - National Bureau of Economic Research - 2021

We develop new semiparametric methods for estimating treatment effects. We focus on a setting where the outcome distributions may be thick tailed, where treatment effects are small, where sample sizes are large and where assignment is completely random. This setting is of particular interest in...

Persistent link: https://www.econbiz.de/10012629462

Semiparametric Causality Tests Using the Policy Propensity Score

Angrist, Joshua D. - 2004

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/10012467713

Nonlinear Policy Rules and the Identification and Estimation of Causal Effects in a Generalized Regression Kink Design

Card, David - 2012

We consider nonparametric identification and estimation in a nonseparable model where a continuous regressor of interest is a known, deterministic, but kinked function of an observed assignment variable. This design arises in many institutional settings where a policy variable (such as weekly...

Persistent link: https://www.econbiz.de/10012460096

Endogenous Stratification in Randomized Experiments

Abadie, Alberto - 2013

Researchers and policy makers are often interested in estimating how treatments or policy interventions affect the outcomes of those most in need of help. This concern has motivated the increasingly common practice of disaggregating experimental data by groups constructed on the basis of an...

Persistent link: https://www.econbiz.de/10012458921

Causal Inference from Hypothetical Evaluations

Bernheim, Bert Douglas; Björkegren, Daniel; Naecker, … - National Bureau of Economic Research - 2021

This paper explores methods for inferring the causal effects of treatments on choices by combining data on real choices with hypothetical evaluations. We propose a class of estimators, identify conditions under which they yield consistent estimates, and derive their asymptotic distributions. The...

Persistent link: https://www.econbiz.de/10012794643

Combining Matching and Synthetic Controls 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. We show how to use a rolling …

Persistent link: https://www.econbiz.de/10012479148

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/10012458705

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

Heckman, James J. - 2003

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/10012469205

Efficient Estimation of a Dynamic Error-Shock Model

Hsiao, Cheng - 1976

This paper is concerned with the estimation of the parameters in a dynamic simultaneous equation model with stationary disturbances under the assumption that the variables are subject to random measurement errors. The conditions under which the parameters are identified are stated. An...

Persistent link: https://www.econbiz.de/10012478972

Nonparametric Estimates of Demand in the California Health Insurance Exchange

Tebaldi, Pietro - 2019

We estimate the demand for health insurance in the California Affordable Care Act marketplace (Covered California) without using parametric assumptions about the unobserved components of utility. To do this, we develop a computational method for constructing sharp identified sets in a...

Persistent link: https://www.econbiz.de/10012479776

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