scholarly journals Multiple imputation regression discontinuity designs: Alternative to regression discontinuity designs to estimate the local average treatment effect at the cutoff

2021 ◽  
pp. 1-20
Author(s):  
Masayoshi Takahashi
Keyword(s):  
Multiple Imputation ◽  
Treatment Effect ◽  
Regression Discontinuity ◽  
Average Treatment Effect ◽  
Average Treatment ◽  
Local Average Treatment Effect ◽  
Regression Discontinuity Designs ◽  
Local Average

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 Regression Discontinuity Designs with an Ordinal Running Variable: Evaluating the Effects of Extended Time Accommodations for English Language Learners

2021 ◽  
Author(s):  
Youmi Suk ◽  
Peter Steiner ◽  
Jee-Seon Kim ◽  
Hyunseung Kang
Keyword(s):  
English Language Learners ◽  
Language Learners ◽  
Treatment Effect ◽  
English Language ◽  
Regression Discontinuity ◽  
Average Treatment Effect ◽  
Extended Time ◽  
Treatment Assignment ◽  
Average Treatment ◽  
Regression Discontinuity Designs

Regression discontinuity designs are commonly used for program evaluation with continuous treatment assignment variables. But in practice, treatment assignment is frequently based on discrete or ordinal variables. In this study, we propose a regression discontinuity design with an ordinal running variable to assess the effects of extended time accommodations (ETA) for English language learners (ELL). ETA eligibility is determined by ordinal ELL English proficiency categories of National Assessment of Educational Progress data. We discuss the identification and estimation of the average treatment effect, intent-to-treat effect, and the local average treatment effect at the cutoff. We also propose a series of sensitivity analyses to probe the effect estimates' robustness to the choices of scaling functions and cutoff scores, and unmeasured confounding.

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.

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