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Balancing Score Adjusted Targeted Minimum Loss-based Estimation

Lendle, Samuel David; Fireman, Bruce; van der Laan, Mark J. - In: Journal of Causal Inference 3 (2015) 2, pp. 139-155

property, and discuss a class of estimators that have this property. We introduce a targeted minimum loss-based estimator (TMLE …. We investigate the new estimator’s performance relative to other estimators, including another TMLE, a propensity score …

Persistent link: https://www.econbiz.de/10014610792

Targeted Data Adaptive Estimation of the Causal Dose–Response Curve

Díaz, Iván; van der Laan, Mark J. - In: Journal of Causal Inference 1 (2013) 2, pp. 171-192

minimum loss–based estimator (CV-TMLE) counterpart. These estimators are proven consistent and efficient under certain … estimators of the treatment and outcome mechanisms. Because the CV-TMLE is a substitution estimator, it is more robust than the … the CV-TMLE and the CV-A-IPTW are explored in a simulation study. …

Persistent link: https://www.econbiz.de/10014610786

Predicting Is Not Explaining: Targeted Learning of the Dative Alternation

Chambaz, Antoine; Desagulier, Guillaume - In: Journal of Causal Inference 4 (2016) 1, pp. 1-30

estimation (TMLE). Through causal analysis, we operationalize the set of scientific questions that we wish to address regarding … the dative alternation. Drawing on the philosophy of TMLE, we answer these questions by targeting some versatile machine …

Persistent link: https://www.econbiz.de/10014610820

Semi-Parametric Estimation and Inference for the Mean Outcome of the Single Time-Point Intervention in a Causally Connected Population

Sofrygin, Oleg; van der Laan, Mark J. - In: Journal of Causal Inference 5 (2016) 1

recent methodological advances in the field of targeted maximum likelihood estimation (TMLE) and describe an estimation …-generating distribution. With this key insight in mind, we describe the TMLE for possibly-dependent units as an iid data algorithm and we …

Persistent link: https://www.econbiz.de/10014610840

A fundamental measure of treatment effect heterogeneity

Levy, Jonathan; van der Laan, Mark; Hubbard, Alan; … - In: Journal of Causal Inference 9 (2021) 1, pp. 83-108

values. We offer a non-parametric plug-in estimator, the targeted maximum likelihood estimator (TMLE) and the cross …-validated TMLE (CV-TMLE), to simultaneously estimate both the average and variance of the stratum-specific treatment effect function …. The CV-TMLE is preferable because it guarantees asymptotic efficiency under two conditions without needing entropy …

Persistent link: https://www.econbiz.de/10014610883

Analysis of Subgroup Data of Clinical Trials

Tsai, Kao-Tai; Peace, Karl - In: Journal of Causal Inference 1 (2013) 2, pp. 193-207

Abstract Large randomized controlled clinical trials are the gold standard to evaluate and compare the effects of treatments. It is common practice for investigators to explore and even attempt to compare treatments, beyond the first round of primary analyses, for various subsets of the study...

Persistent link: https://www.econbiz.de/10014610788

Propensity Score Analysis with Survey Weighted Data

Ridgeway, Greg; Kovalchik, Stephanie Ann; Griffin, Beth Ann - In: Journal of Causal Inference 3 (2015) 2, pp. 237-249

Abstract Propensity score analysis (PSA) is a common method for estimating treatment effects, but researchers dealing with data from survey designs are generally not properly accounting for the sampling weights in their analyses. Moreover, recommendations given in the few existing methodological...

Persistent link: https://www.econbiz.de/10014610821

Chasing Balance and Other Recommendations for Improving Nonparametric Propensity Score Models

Griffin, Beth Ann; McCaffrey, Daniel F.; Almirall, Daniel; … - In: Journal of Causal Inference 5 (2017) 2

Abstract: In this article, we carefully examine two important implementation issues when estimating propensity scores using generalized boosted models (GBM), a promising machine learning technique. First, we examine which of the following methods for tuning GBM lead to better covariate balance...

Persistent link: https://www.econbiz.de/10014610835

Variable Selection in Causal Inference using a Simultaneous Penalization Method

Ertefaie, Ashkan; Asgharian, Masoud; Stephens, David A. - In: Journal of Causal Inference 6 (2018) 1

Abstract In the causal adjustment setting, variable selection techniques based only on the outcome or only on the treatment allocation model can result in the omission of confounders and hence may lead to bias, or the inclusion of spurious variables and hence cause variance inflation, in...

Persistent link: https://www.econbiz.de/10014610859