Abstract Estimating the effect of a randomized treatment and the effect that is transmitted through a mediator is often complicated by treatment noncompliance. In literature, an instrumental variable (IV)-based method has been developed to study causal mediation effects in the presence of...

Abstract Direct effects in mediation analysis quantify the effect of an exposure on an outcome not mediated by a certain intermediate. When estimating direct effects through measured data, misclassification may occur in the outcomes, exposures, and mediators. In mediation analysis, any such...

Abstract Unmeasured confounding is one of the most important threats to the validity of observational studies. In this paper we scrutinize a recently proposed sensitivity analysis for unmeasured confounding. The analysis requires specification of two parameters, loosely defined as the maximal...

Abstract The increasing availability of passively observed data has yielded a growing interest in “data fusion” methods, which involve merging data from observational and experimental sources to draw causal conclusions. Such methods often require a precarious tradeoff between the unknown...

Abstract Unmeasured confounding is an important threat to the validity of observational studies. A common way to deal with unmeasured confounding is to compute bounds for the causal effect of interest, that is, a range of values that is guaranteed to include the true effect, given the observed...

Abstract We present a method for assessing the sensitivity of the true causal effect to unmeasured confounding. The method requires the analyst to set two intuitive parameters. Otherwise, the method is assumption free. The method returns an interval that contains the true causal effect and whose...

Abstract Residual confounding is a common source of bias in observational studies. In this article, we build upon a series of sensitivity analyses methods for residual confounding developed by Brumback et al. and Chiba whose sensitivity parameters are constructed to quantify deviation from...

Abstract This paper describes and implements a simple partial solution to the most common problem in applied microeconometrics: estimating a linear causal effect with a potentially endogenous explanatory variable and no suitable instrumental variables. Empirical researchers faced with this...

Policy analysts involved in quantitative research have many options for handling missing data. The method chosen will often greatly influence the substantive policy conclusions that will be drawn from the data. The most frequent methods for handling missing data assume that the data are missing...

If geothermal heat pumps (GHPs) are to make a significant mark in the market, we believe that it will be through energy service pricing contracts offered by retailcos. The benefits of GHPs are ideally suited to energy service pricing (ESP) contractual arrangements; however, few retailcos are...