3 accepted papers
Predictive models are often deployed for decision-making tasks for which they were not explicitly trained. When only partial observations of the relevant state are available, as in most real-world applications, there is a strong possibility of hidden confounding. Therefore, partial observability oft
A largely unaddressed problem in causal inference is that of learning reliable policies in continuous, high-dimensional treatment variables from observational data. Especially in the presence of strong confounding, it can be infeasible to learn the entire heterogeneous response surface from treatmen…
Inferring causal effects of continuous-valued treatments from observational data is a crucial task promising to better inform policy- and decision-makers. A critical assumption needed to identify these effects is that all confounding variables—causal parents of both the treatment and the outcome—are…