Doubly robust estimation of the weighted average treatment effect for a target population

2018 ◽  
Vol 38 (3) ◽  
pp. 315-325
Author(s):  
Yebin Tao ◽  
Haoda Fu
Keyword(s):  
Robust Estimation ◽  
Treatment Effect ◽  
Weighted Average ◽  
Target Population ◽  
Average Treatment Effect ◽  
Average Treatment ◽  
Doubly Robust Estimation ◽  
Doubly Robust

A doubly robust weighting estimator of the average treatment effect on the treated

Stat ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. e205 ◽  
Author(s):  
Erica E. M. Moodie ◽  
Olli Saarela ◽  
David A. Stephens
Keyword(s):  
Treatment Effect ◽  
Average Treatment Effect ◽  
Average Treatment ◽  
Doubly Robust

scholarly journals Generalizability of Randomized Trial Results to Target Populations

2017 ◽  
Vol 28 (5) ◽  
pp. 532-537 ◽  
Author(s):  
Elizabeth A. Stuart ◽  
Benjamin Ackerman ◽  
Daniel Westreich
Keyword(s):  
Social Work ◽  
Randomized Trial ◽  
Treatment Effect ◽  
Randomized Trials ◽  
Internal Validity ◽  
Target Population ◽  
Average Treatment Effect ◽  
Social Work Research ◽  
Average Treatment ◽  
Social Work Program

Randomized trials play an important role in estimating the effect of a policy or social work program in a given population. While most trial designs benefit from strong internal validity, they often lack external validity, or generalizability, to the target population of interest. In other words, one can obtain an unbiased estimate of the study sample average treatment effect from a randomized trial; however, this estimate may not equal the target population average treatment effect if the study sample is not fully representative of the target population. This article provides an overview of existing strategies to assess and improve upon the generalizability of randomized trials, both through statistical methods and study design, as well as recommendations on how to implement these ideas in social work research.

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 Instruments with Heterogeneous Effects: Bias, Monotonicity, and Localness

2020 ◽  
Vol 8 (1) ◽  
pp. 182-208
Author(s):  
Nick Huntington-Klein
Keyword(s):  
Treatment Effect ◽  
Treatment Effects ◽  
Weighted Average ◽  
Small Sample ◽  
Average Treatment Effect ◽  
Average Treatment ◽  
Local Average Treatment Effect ◽  
Heterogeneous Effects ◽  
The Impact ◽  
Local Average

AbstractIn Instrumental Variables (IV) estimation, the effect of an instrument on an endogenous variable may vary across the sample. In this case, IV produces a local average treatment effect (LATE), and if monotonicity does not hold, then no effect of interest is identified. In this paper, I calculate the weighted average of treatment effects that is identified under general first-stage effect heterogeneity, which is generally not the average treatment effect among those affected by the instrument. I then describe a simple set of data-driven approaches to modeling variation in the effect of the instrument. These approaches identify a Super-Local Average Treatment Effect (SLATE) that weights treatment effects by the corresponding instrument effect more heavily than LATE. Even when first-stage heterogeneity is poorly modeled, these approaches considerably reduce the impact of small-sample bias compared to standard IV and unbiased weak-instrument IV methods, and can also make results more robust to violations of monotonicity. In application to a published study with a strong instrument, the preferred approach reduces error by about 19% in small (N ≈ 1, 000) subsamples, and by about 13% in larger (N ≈ 33, 000) subsamples.

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