Causal inference methods to study nonrandomized, preexisting development interventions

Causal inference methods to study nonrandomized, preexisting development interventions.

Empirical measurement of interventions to address significant global health and development problems is necessary to ensure that resources are applied appropriately. Such intervention programs are often deployed at the group or community level. The gold standard design to measure the effectiveness of community-level interventions is the community-randomized trial, but the conditions of these tr...

Interventions and Causal Inference

The literature on causal discovery has focused on interventions that involve randomly assigning values to a single variable. But such a randomized intervention is not the only possibility, nor is it always optimal. In some cases it is impossible or it would be unethical to perform such an intervention. We provide an account of “hard” and “soft” interventions, and discuss what they can contribut...

Causal Bandits: Learning Good Interventions via Causal Inference

We study the problem of using causal models to improve the rate at which good interventions can be learned online in a stochastic environment. Our formalism combines multi-arm bandits and causal inference to model a novel type of bandit feedback that is not exploited by existing approaches. We propose a new algorithm that exploits the causal feedback and prove a bound on its simple regret that ...

Matching Methods for Causal Inference

Complete Identi cation Methods for Causal Inference

of the Dissertation Complete Identification Methods for Causal Inference