scholarly journals On Heckits, LATE, and Numerical Equivalence

Econometrica ◽  
2019 ◽  
Vol 87 (2) ◽  
pp. 677-696 ◽  
Author(s):  
Patrick Kline ◽  
Christopher R. Walters
Keyword(s):  
Functional Form ◽  
Control Function ◽  
Maximum Likelihood Estimators ◽  
Average Treatment Effect ◽  
Econometric Methods ◽  
Local Average Treatment Effect ◽  
Threshold Crossing ◽  
Numerical Equivalence ◽  
Structural Econometric ◽  
Local Average

Structural econometric methods are often criticized for being sensitive to functional form assumptions. We study parametric estimators of the local average treatment effect (LATE) derived from a widely used class of latent threshold crossing models and show they yield LATE estimates algebraically equivalent to the instrumental variables (IV) estimator. Our leading example is Heckman's (1979) two‐step (“Heckit”) control function estimator which, with two‐sided non‐compliance, can be used to compute estimates of a variety of causal parameters. Equivalence with IV is established for a semiparametric family of control function estimators and shown to hold at interior solutions for a class of maximum likelihood estimators. Our results suggest differences between structural and IV estimates often stem from disagreements about the target parameter rather than from functional form assumptions per se. In cases where equivalence fails, reporting structural estimates of LATE alongside IV provides a simple means of assessing the credibility of structural extrapolation exercises.

OLS and 2SLS in Randomized and Conditionally Randomized Experiments

2018 ◽  
Vol 238 (3-4) ◽  
pp. 243-293 ◽  
Author(s):  
Jason Ansel ◽  
Han Hong ◽  
and Jessie Li
Keyword(s):  
Treatment Effect ◽  
Average Treatment Effect ◽  
Randomized Experiments ◽  
Adaptive Randomization ◽  
Working Paper ◽  
Average Treatment ◽  
Local Average Treatment Effect ◽  
Effect Parameter ◽  
Multiple Treatments ◽  
Local Average

Abstract We investigate estimation and inference of the (local) average treatment effect parameter when a binary instrumental variable is generated by a randomized or conditionally randomized experiment. Under i.i.d. sampling, we show that adding covariates and their interactions with the instrument will weakly improve estimation precision of the (local) average treatment effect, but the robust OLS (2SLS) standard errors will no longer be valid. We provide an analytic correction that is easy to implement and demonstrate through Monte Carlo simulations and an empirical application the interacted estimator’s efficiency gains over the unadjusted estimator and the uninteracted covariate adjusted estimator. We also generalize our results to covariate adaptive randomization where the treatment assignment is not i.i.d., thus extending the recent contributions of Bugni, F., I.A. Canay, A.M. Shaikh (2017a), Inference Under Covariate-Adaptive Randomization. Working Paper and Bugni, F., I.A. Canay, A.M. Shaikh (2017b), Inference Under Covariate-Adaptive Randomization with Multiple Treatments. Working Paper to allow for the case of non-compliance.

scholarly journals Too LATE for Natural Experiments: A Critique of Local Average Treatment Effects Using the Example of Angrist and Evans (1998)

2019 ◽  
Author(s):  
Stefan Öberg
Keyword(s):  
Indirect Effect ◽  
Instrumental Variable ◽  
Natural Experiment ◽  
Average Treatment Effect ◽  
Natural Experiments ◽  
Average Treatment ◽  
Local Average Treatment Effect ◽  
Average Treatment Effects ◽  
Local Average Treatment Effects ◽  
Local Average

There has been a fundamental flaw in the conceptual design of many natural experiments used in the economics literature, particularly among studies aiming to estimate a local average treatment effect (LATE). When we use an instrumental variable (IV) to estimate a LATE, the IV only has an indirect effect on the treatment of interest. Such IVs do not work as intended and will produce severely biased and/or uninterpretable results. This comment demonstrates that the LATE does not work as previously thought and explains why using the natural experiment proposed by Angrist and Evans (1998) as the example.

scholarly journals Profiling Compliers and Noncompliers for Instrumental-Variable Analysis

2020 ◽  
Vol 28 (3) ◽  
pp. 435-444 ◽  
Author(s):  
Moritz Marbach ◽  
Dominik Hangartner
Keyword(s):  
Instrumental Variable ◽  
Average Treatment Effect ◽  
Instrumental Variable Analysis ◽  
Treatment Variation ◽  
Local Average Treatment Effect ◽  
Outcome Framework ◽  
The Social ◽  
Exogenous Treatment ◽  
Local Average ◽  
General Method

Instrumental-variable (IV) estimation is an essential method for applied researchers across the social and behavioral sciences who analyze randomized control trials marred by noncompliance or leverage partially exogenous treatment variation in observational studies. The potential outcome framework is a popular model to motivate the assumptions underlying the identification of the local average treatment effect (LATE) and to stratify the sample into compliers, always-takers, and never-takers. However, applied research has thus far paid little attention to the characteristics of compliers and noncompliers. Yet, profiling compliers and noncompliers is necessary to understand what subpopulation the researcher is making inferences about and an important first step in evaluating the external validity (or lack thereof) of the LATE estimated for compliers. In this letter, we discuss the assumptions necessary for profiling, which are weaker than the assumptions necessary for identifying the LATE if the instrument is randomly assigned. We introduce a simple and general method to characterize compliers, always-takers, and never-takers in terms of their covariates and provide easy-to-use software in R and STATA that implements our estimator. We hope that our method and software facilitate the profiling of compliers and noncompliers as a standard practice accompanying any IV analysis.

scholarly journals Estimating the Causal Effect of Gun Prevalence on Homicide Rates: A Local Average Treatment Effect Approach

2012 ◽  
Vol 29 (4) ◽  
pp. 477-541 ◽  
Author(s):  
Tomislav Kovandzic ◽  
Mark E. Schaffer ◽  
Gary Kleck
Keyword(s):  
Treatment Effect ◽  
Causal Effect ◽  
Average Treatment Effect ◽  
Homicide Rates ◽  
Average Treatment ◽  
Local Average Treatment Effect ◽  
Local Average

scholarly journals Instrumental variable estimation of truncated local average treatment effects

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249642
Author(s):  
Byeong Yeob Choi
Keyword(s):  
Treatment Effect ◽  
Instrumental Variable ◽  
Propensity Scores ◽  
Real Data ◽  
Target Population ◽  
Average Treatment Effect ◽  
Average Treatment ◽  
Local Average Treatment Effect ◽  
Local Average Treatment Effects ◽  
Local Average

Instrumental variable (IV) analysis is used to address unmeasured confounding when comparing two nonrandomized treatment groups. The local average treatment effect (LATE) is a causal estimand that can be identified by an IV. The LATE approach is appealing because its identification relies on weaker assumptions than those in other IV approaches requiring a homogeneous treatment effect assumption. If the instrument is confounded by some covariates, then one can use a weighting estimator, for which the outcome and treatment are weighted by instrumental propensity scores. The weighting estimator for the LATE has a large variance when the IV is weak and the target population, i.e., the compliers, is relatively small. We propose a truncated LATE that can be estimated more reliably than the regular LATE in the presence of a weak IV. In our approach, subjects who contribute substantially to the weak IV are identified by their probabilities of being compliers, and they are removed based on a pre-specified threshold. We discuss interpretation of the proposed estimand and related inference method. Simulation and real data experiments demonstrate that the proposed truncated LATE can be estimated more precisely than the standard LATE.

scholarly journals Testing Local Average Treatment Effect Assumptions

2017 ◽  
Vol 99 (2) ◽  
pp. 305-313 ◽  
Author(s):  
Ismael Mourifié ◽  
Yuanyuan Wan
Keyword(s):  
Treatment Effect ◽  
Average Treatment Effect ◽  
Average Treatment ◽  
Local Average Treatment Effect ◽  
Local Average
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