This paper proposes strategies for defining, identifying, and estimating features of treatment-effect distributions in contexts where multiple outcomes are of interest. After describing existing empirical approaches used in such settings, the paper develops a notion of treatment preference that...

Comparing median outcomes to gauge treatment effectiveness is widespread practice in clinical and other investigations. While common, such difference-in-median characterizations of effectiveness are but one way to summarize how outcome distributions compare. This paper explores properties of...

This paper suggests analytical strategies for obtaining informative parameter bounds when multivariate health-outcome data are partially observed in a particular yet common manner. One familiar context is where M1 health outcomes' respective totals across N1 time periods are observed but where...

With count-valued outcomes y in {0,1,...,M} identification and estimation of average treatment effects raise no special considerations beyond those involved in the continuous-outcome case. If partial identification of the distribution of treatment effects is of interest, however, count-valued...

This paper presents and assesses analytical strategies that respect the bounded count structures of outcomes that are encountered often in health and other applications. The paper's main motivation is that the applied econometrics literature lacks a comprehensive discussion and critique of...

A common practice in evidence-based decision-making uses estimates of conditional probabilities P(y|x) obtained from research studies to predict outcomes y on the basis of observed covariates x. Given this information, decisions are then based on the predicted outcomes. Researchers commonly...

The use of race measures in clinical prediction models and algorithms has become a highly contentious issue, driven by concerns that inclusion of race as a covariate exacerbates and perpetuates long-standing disparities in quality of health care provided to racial and ethnic minority patients....

Dependent variables that are non-negative, follow right-skewed distributions, and have large probability mass at zero arise often in empirical economics. Two classes of models that transform the dependent variable y -- the natural logarithm of y plus a constant and the inverse hyperbolic sine --...

Models having multivariate probit and related structures arise often in applied health economics. When the outcome dimensions of such models are large, however, estimation can be challenging owing to numerical computation constraints and/or speed. This paper suggests the utility of estimating...

Estimation of marginal or partial effects of covariates x on various conditional parameters or functionals is often the main target of applied microeconometric analysis. In the specific context of probit models such estimation is straightforward in univariate models, and Greene, 1996, 1998, has...