Beyond LATE: Estimation of the Average Treatment Effect with an Instrumental Variable

2013 ◽  
Vol 21 (4) ◽  
pp. 492-506 ◽  
Author(s):  
Peter M. Aronow ◽  
Allison Carnegie
Keyword(s):  
Treatment Effect ◽  
Instrumental Variable ◽  
Causal Effect ◽  
Average Treatment Effect ◽  
Treatment Variable ◽  
Estimation Strategy ◽  
Average Treatment ◽  
Local Average Treatment Effect ◽  
Receive Treatment ◽  
Local Average

Political scientists frequently use instrumental variables (IV) estimation to estimate the causal effect of an endogenous treatment variable. However, when the treatment effect is heterogeneous, this estimation strategy only recovers the local average treatment effect (LATE). The LATE is an average treatment effect (ATE) for a subset of the population: units that receive treatment if and only if they are induced by an exogenous IV. However, researchers may instead be interested in the ATE for the entire population of interest. In this article, we develop a simple reweighting method for estimating the ATE, shedding light on the identification challenge posed in moving from the LATE to the ATE. We apply our method to two published experiments in political science in which we demonstrate that the LATE has the potential to substantively differ from the ATE.

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 Intangible Capital and Leverage

2020 ◽  
pp. 1-24 ◽  
Author(s):  
Philipp Horsch ◽  
Philip Longoni ◽  
David Oesch
Keyword(s):  
Treatment Effect ◽  
Causal Effect ◽  
Average Treatment Effect ◽  
Intangible Capital ◽  
Average Treatment ◽  
Local Average Treatment Effect ◽  
Local Average

We investigate the causal effect of intangible capital on leverage. To address endogeneity, we exploit patent invalidations by a U.S. court in which judges are randomly assigned to cases. Differences in judge leniency provide exogenous variation in the probability that firms’ patents are invalidated. Using this probability as an instrument for exogenous losses in intangible capital, we find a patent invalidation leads to a 14.1% reduction in leverage, suggesting that intangible capital causally supports leverage. This local average treatment effect is stronger in firms that use patents as loan collateral and in less creditworthy as well as smaller firms.

Estimation of local treatment effects under the binary instrumental variable model

Biometrika ◽  
2021 ◽  
Author(s):  
Linbo Wang ◽  
Yuexia Zhang ◽  
Thomas S Richardson ◽  
James M Robins
Keyword(s):  
Treatment Effect ◽  
Instrumental Variable ◽  
Local Treatment ◽  
Real Data ◽  
Average Treatment Effect ◽  
Variable Model ◽  
Average Treatment ◽  
Local Average Treatment Effect ◽  
Local Average ◽  
Instrumental Variable Model

Abstract Instrumental variables are widely used to deal with unmeasured confounding in observational studies and imperfect randomized controlled trials. In these studies, researchers often target the so-called local average treatment effect as it is identifiable under mild conditions. In this paper, we consider estimation of the local average treatment effect under the binary instrumental variable model. We discuss the challenges for causal estimation with a binary outcome, and show that surprisingly, it can be more difficult than the case with a continuous outcome. We propose novel modelling and estimating procedures that improve upon existing proposals in terms of model congeniality, interpretability, robustness and efficiency. Our approach is illustrated via simulation studies and a real data analysis.

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.

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