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Sensitivity Analysis for Average Treatment Effects [PDF]
The Stata Journal: Promoting communications on statistics and Stata, 2007 Based on the conditional independence or unconfoundedness assumption, matching has become a popular approach to estimate average treatment effects. Checking the sensitivity of the estimated results with respect to deviations from this identifying assumption has become an increasingly important topic in the applied evaluation literature.
Sascha O. Becker, Marco Caliendo
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Information Bottleneck for Estimating Treatment Effects with Systematically Missing Covariates
Entropy, 2020 Estimating the effects of an intervention from high-dimensional observational data is a challenging problem due to the existence of confounding. The task is often further complicated in healthcare applications where a set of observations may be entirely ...
Sonali Parbhoo +3 more
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Individualized treatment rules under stochastic treatment cost constraints
Journal of Causal Inference, 2022 Estimation and evaluation of individualized treatment rules have been studied extensively, but real-world treatment resource constraints have received limited attention in existing methods.
Qiu Hongxiang +2 more
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Frontiers in Sustainable Food Systems, 2022 Drought is one of the major abiotic constraints that adversely affect maize production in the rain-fed agro-environment in the Asian tropics. In view of the recurrent drought, stress-resilient (SR) maize hybrids were developed and deployed to minimize ...
Atul P. Kulkarni +4 more
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Mathematics, 2022 Regression adjustment is often used to estimate average treatment effect (ATE) in randomized experiments. Recently, some penalty-based regression adjustment methods have been proposed to handle the high-dimensional problem.
Zeyu Diao +3 more
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Identification and Estimation of Local Average Treatment Effects [PDF]
Econometrica, 1994 We investigate conditions sufficient for identification of average treatment effects using instrumental variables. First we show that the existence of valid instruments is not sufficient to identify any meaningful average treatment effect. We then establish that the combination of an instrument and a condition on the relation between the instrument and
Joshua D. Angrist, Guido W. Imbens
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Causal effect on a target population: A sensitivity analysis to handle missing covariates
Journal of Causal Inference, 2022 Randomized controlled trials (RCTs) are often considered the gold standard for estimating causal effect, but they may lack external validity when the population eligible to the RCT is substantially different from the target population.
Colnet Bénédicte +3 more
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Testing for the Unconfoundedness Assumption Using an Instrumental Assumption
Journal of Causal Inference, 2014 The identification of average causal effects of a treatment in observational studies is typically based either on the unconfoundedness assumption (exogeneity of the treatment) or on the availability of an instrument.
de Luna Xavier, Johansson Per
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Optimal weighting for estimating generalized average treatment effects
Journal of Causal Inference, 2022 In causal inference, a variety of causal effect estimands have been studied, including the sample, uncensored, target, conditional, optimal subpopulation, and optimal weighted average treatment effects.
Kallus Nathan, Santacatterina Michele
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Journal of Causal Inference, 2015 Failure (or success) in finding a statistically significant effect of a large-scale intervention may be due to choices made in the evaluation. To highlight the potential limitations and pitfalls of some common identification strategies used for ...
Weber Ann M. +2 more
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