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1 Introduction Inferring cause-and-effect relationships between variables is of primary importance in many fields of science. The classical approach for determining such relationships uses randomized experiments where a single or few variables are perturbed. Such intervention experiments, however, can be very expensive, unethical (e.g. one cannot force a randomly selected person to smoke many cigarettes a day) or even infeasible. Hence, it is desirable to infer causal effects from so-called observational data obtained by observing a system without subjecting it

1 Introduction When causal effects are heterogeneous, then inferences depend on the population for which causal effects are estimated. Although population average causal effects have traditionally been the inferential targets, recent results have focused on estimating average causal effects that are local to some subpopulation for reasons of efficiency. These approaches include trimming observations based on the distribution of the propensity score [ 1 ], using regression adjustment to estimate reweighted causal effects [ 2 , 3 , 4 ], or implementing calipers

subject to right censoring, which has not been considered in previous studies of Mann–Whitney-type causal effects. If T is a survival time, θ remains well defined and is just as meaningful as it is for another continuous outcome. In fact, the class of Mann–Whitney-type causal effects includes some well known and commonly used effect measures for survival outcomes. For example, if h ( t 1 , t 0 ) = I ( t 1 ≤ t ) − I ( t 0 ≤ t ) h({t_{1}},{t_{0}})=I({t_{1}}\le t)-I({t_{0}}\le t) , then θ = F 1 ( t ) − F 0 ( t ) \theta ={F_{1}}(t)-{F_{0}}(t) , where F a ( t ) = P

Volume 8, Issue 1 2012 Article 24 The International Journal of Biostatistics Instruments and Bounds for Causal Effects under the Monotonic Selection Assumption Masataka Taguri, Yokohama City University Yasutaka Chiba, Kinki University School of Medicine Recommended Citation: Taguri, Masataka and Chiba, Yasutaka (2012) "Instruments and Bounds for Causal Effects under the Monotonic Selection Assumption," The International Journal of Biostatistics: Vol. 8: Iss. 1, Article 24. DOI: 10.1515/1557-4679.1386 ©2012 De Gruyter. All rights reserved. Instruments and Bounds

Volume 8, Issue 1 2012 Article 9 The International Journal of Biostatistics Targeted Minimum Loss Based Estimation of Causal Effects of Multiple Time Point Interventions Mark J. van der Laan, University of California - Berkeley Susan Gruber, Harvard University Recommended Citation: van der Laan, Mark J. and Gruber, Susan (2012) "Targeted Minimum Loss Based Estimation of Causal Effects of Multiple Time Point Interventions," The International Journal of Biostatistics: Vol. 8: Iss. 1, Article 9. DOI: 10.1515/1557-4679.1370 ©2012 De Gruyter. All rights reserved

The Causal Effects of the Minimum Wage Introduction in Germany – An Overview Marco Caliendo University of Potsdam, IZA, DIW, IAB Carsten Schr€oder SOEP at DIW, Free University Berlin, Linda Wittbrodt University of Potsdam Abstract. In 2015, Germany introduced a statutory hourly minimum wage that was not only universally binding but also set at a relatively high level. We discuss the short- run effects of this new minimum wage on a wide set of socioeconomic outcomes, such as employment and working hours, earnings and wage inequality, dependent and self- employment

1 Introduction The main purpose of randomized trials is to draw inferences regarding causal effects. When two treatment arms are compared with binary outcomes, causal effects can be identified by assuming that the two treatment arms are exchangeable. This assumption means that the risk of the event in the treatment arm would have been the same as the risk of the event in the control arm had subjects in the treatment arm been assigned to the control arm [ 1 ], [ 2 ], and it is often assumed under random assignment. In trials with an ordinal outcome, which is

preference trials” encapsulate a variety of research designs in which patients’ preferences are elicited, some individuals are randomized, and some receive a treatment of their choosing. In the recent proposal of [ 13 ], treatment preferences are elicited from all individuals, who are randomized into one group that will have their treatment assigned at random, and one that can choose its own treatment. This design allows for sharp-bound identification and sensitivity analysis for the average causal effects among those who would choose a given treatment. The present method

]. In a recent article, Ertefaie et al. [ 19 ] developed a method for estimating the propensity score in the presence of length-biased sampling. In this paper, we address estimation of total causal effects in the presence of both length-biased sampling and confounding, which we term the double-bias problem, in the analysis of survival data. Specifically, we develop augmented estimating equations based on PH and accelerated failure time (AFT) models that can be used to estimate the exposure effect. In both cases, the augmentation spaces are formed using the censoring

estimation of causal effects from right-censored survival data therefore requires proper adjustment for both confounding due to covariate imbalances in treatment groups and confounding due to time-dependent censoring. With complex medical data, it is often preferred to work under a nonparametric statistical model. In light of this, many authors in the medical literature construct the Kaplan-Meier (KM) [ 1 ] product limit estimator stratified within each treatment group. This procedure yields a consistent estimation of the treatment-specific marginal survival probabilities