Inference on Difference-in-Differences average treatment effects: A fixed-b approach
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Econometrics"
DOI: 10.1016/j.jeconom.2019.04.001
Abstract: Abstract This paper provides an analysis of the standard errors proposed by Driscoll and Kraay (1998) (DK) in linear Difference-in-Differences (DD) models with fixed effects and individual-specific time trends. The analysis is accomplished within the… read more here.
Keywords: average treatment; difference differences; inference difference; fixed asymptotic ... See more keywords
Inference on a New Class of Sample Average Treatment Effects
Sign Up to like & getrecommendations! Published in 2020 at "Journal of the American Statistical Association"
DOI: 10.1080/01621459.2020.1730854
Abstract: Abstract We derive new variance formulas for inference on a general class of estimands of causal average treatment effects in a randomized control trial. We generalize the seminal work of Robins and show that when… read more here.
Keywords: average treatment; class; treatment; inference new ... See more keywords
Partial identification of average treatment effects on the treated through difference-in-differences
Sign Up to like & getrecommendations! Published in 2017 at "Econometric Reviews"
DOI: 10.1080/07474938.2017.1308036
Abstract: ABSTRACT The difference-in-differences (DID) method is widely used as a tool for identifying causal effects of treatments in program evaluation. When panel data sets are available, it is well-known that the average treatment effect on… read more here.
Keywords: difference differences; average treatment; partial identification; treatment ... See more keywords
Estimating local average treatment effects in aggregate data
Sign Up to like & getrecommendations! Published in 2017 at "Applied Economics Letters"
DOI: 10.1080/13504851.2016.1226483
Abstract: ABSTRACT In some contexts, the effect of a treatment can be estimated with easily accessible aggregate rather than individual data, using difference-in-difference estimation. However, under imperfect assignment within groups, this produces intent-to-treat estimates, which may… read more here.
Keywords: local average; average treatment; estimating local; treatment ... See more keywords
Estimands in cluster-randomized trials: choosing analyses that answer the right question
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Epidemiology"
DOI: 10.1093/ije/dyac131
Abstract: Abstract Background Cluster-randomized trials (CRTs) involve randomizing groups of individuals (e.g. hospitals, schools or villages) to different interventions. Various approaches exist for analysing CRTs but there has been little discussion around the treatment effects (estimands)… read more here.
Keywords: average treatment; treatment effect; question; treatment ... See more keywords
Two-stage g-computation
Sign Up to like & getrecommendations! Published in 2020 at "Epidemiology"
DOI: 10.1097/ede.0000000000001233
Abstract: Supplemental Digital Content is available in the text. Illustrations of the g-computation algorithm to evaluate population average treatment and intervention effects have been predominantly implemented in settings with complete exposure information. Thus, worked examples of… read more here.
Keywords: average treatment; stage computation; computation; two stage ... See more keywords
Conditions for Generating Treatment Effect Estimates in Line With Objectives: Beyond Confounding.
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DOI: 10.1097/mlr.0000000000000614
Abstract: D iscussions around the validity of treatment effect estimates in comparative effectiveness research (CER) commonly focus on general concepts of bias and, specifically, whether assumptions related to confounding are satisfied. Although necessary, confounding is only… read more here.
Keywords: average treatment; treatment; cer; effect estimates ... See more keywords
Propensity score specification for optimal estimation of average treatment effect with binary response
Sign Up to like & getrecommendations! Published in 2020 at "Statistical Methods in Medical Research"
DOI: 10.1177/0962280220934847
Abstract: Propensity score methods are commonly used in statistical analyses of observational data to reduce the impact of confounding bias in estimations of average treatment effect. While the propensity score is defined as the conditional probability… read more here.
Keywords: propensity; propensity score; average treatment; treatment effect ... See more keywords
Variance estimation for the average treatment effects on the treated and on the controls
Sign Up to like & getrecommendations! Published in 2022 at "Statistical Methods in Medical Research"
DOI: 10.1177/09622802221142532
Abstract: Common causal estimands include the average treatment effect, the average treatment effect of the treated, and the average treatment effect on the controls. Using augmented inverse probability weighting methods, parametric models are judiciously leveraged to… read more here.
Keywords: average treatment; treatment effect; treatment; variance estimation ... See more keywords
Instrumental variable estimation of truncated local average treatment effects
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DOI: 10.1371/journal.pone.0249642
Abstract: 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… read more here.
Keywords: local average; variable estimation; average treatment; instrumental variable ... See more keywords
Framework for Evaluating Potential Causes of Health Risk Factors Using Average Treatment Effect and Uplift Modelling
Sign Up to like & getrecommendations! Published in 2023 at "Algorithms"
DOI: 10.3390/a16030166
Abstract: Acute myeloid leukemia (AML) is a type of blood cancer that affects both adults and children. Benzene exposure has been reported to increase the risk of developing AML in children. The assessment of the potential… read more here.
Keywords: average treatment; using average; health; risk factors ... See more keywords