Estimating causal effects from observational data is not always possible due to confounding. Identifying a set of appropriate covariates (adjustment set) and adjusting for their influence can remove confounding bias; however, such often identifiable alone. Experimental allow unbiased effect estimation, but are typically limited in sample size therefore yield estimates high variance. Moreover, e...

Quantitative trait loci (QTLs) mapping often results in data on a number of traits that have well-established causal relationships. Many multi-trait QTL mapping methods that account for correlation among the multiple traits have been developed to improve the statistical power and the precision of QTL parameter estimation. However, none of these methods are capable of incorporating the causal st...

This paper introduces a machine learning approach to quantify altruism from the linguistic style of textual documents. We apply our method central question in (social) entrepreneurship: How does impact entrepreneurial success? Specifically, we examine effects on crowdfunding outcomes Initial Coin Offerings (ICOs). The main result suggests that and ICO firm valuation are negatively related. We, ...

Estimates of additive interaction from case-control data are often obtained by logistic regression; such models can also be used to adjust for covariates. This approach to estimating additive interaction has come under some criticism because of possible misspecification of the logistic model: If the underlying model is linear, the logistic model will be misspecified. The authors propose an inve...

Much of epidemiology and clinical medicine is focused on estimating the effects of treatments or interventions administered over time. In such settings of longitudinal treatment, time-dependent confounding is often an important source of bias. Marginal structural models (MSMs) are a powerful tool for estimating the causal effect of a treatment using observational data, particularly when time-de...

Causal estimates can be obtained by instrumental variable analysis using a two-stage method. However, these can be biased when the instruments are weak. We introduce a Bayesian method, which adjusts for the first-stage residuals in the second-stage regression and has much improved bias and coverage properties. In the continuous outcome case, this adjustment reduces median bias from weak instrum...