Standard bandit algorithms for online policy learning assume the reward is always observed, but in many applications—such as gauging satisfaction through voluntary review prompts—feedback is missing in ways correlated with the outcome. In this regime, I show that the classic UCB algorithm selects suboptimal policies, and introduce a nonparametric Doubly-Robust UCB algorithm that models the missingness through covariates. I show that the newly proposed algorithm achieves near-optimal worst-case regret by means of new high-probability bounds for a class of doubly-robust estimators.

Empirical work leveraging regression discontinuity designs routinely examines how treatment effects vary with pretreatment covariates, yet the most common practices are not backed up by formal results. We develop a unified, theoretically grounded framework built on a fully interacted local-linear model, with principled bandwidth selection and robust bias-corrected inference for estimating heterogeneous effects and testing differences across groups.

We introduce rdhte, a package implementing our framework for heterogeneous treatment effects in RD designs.

Using a regression discontinuity design on Italy’s largest firm-subsidy program (Law 488/92), we show that investment subsidies raised a firm's investment, employment, and survival probability. Both objective eligibility rules and local politicians’ discretion selected firms that went on to create more jobs, but discretion did so at a higher cost per job: allocating on objective rules alone would lower the cost per job by 11%, while relying on political discretion alone would raise it by 42%.

We develop prediction intervals that quantify the uncertainty of synthetic control predictions when multiple treated units and staggered adoption of treatment are involved. We provide precise non-asymptotic coverage guarantees and a careful taxonomy of the causal quantities one may target.

We introduce scpi, a package implementing our framework for uncertainty quantification in synthetic controls.

Deferring systems let a machine-learning model hand difficult cases to a human expert, yet their effect on accuracy is usually judged only by looking at the final human–AI performance. We instead propose a novel framework to evaluate the causal impacts on predictive accuracy of introducing a deferring system.

We explain how pretreatment covariates can be used in regression discontinuity designs: to improve the efficiency; to uncover heterogeneous policy effects; to change the RD parameter of interest.

The regression discontinuity estimand is local in nature. I introduce the package getaway, which implements the Angrist–Rokkanen (2015) method to extrapolate RD treatment effects away from the cutoff under an unconfoundedness assumption. I extend their framework to multi-cutoff designs, propose a data-driven algorithm to select covariates that satisfy such condition, and a toolkit for falsification of the identifying assumptions and visualization.

How long migrants choose to stay abroad is linked to their earnings in ways that need not be monotonic. Using recent documented Mexican immigrants, we document a U-shaped relationship between earnings and the intention to settle permanently in the U.S.. We then estimate a dynamic life-cycle model that rationalizes this feature of the data, and use the model to study how changes in Mexican earnings and U.S.–Mexico purchasing-power parity reshape return plans and savings.

Exploiting the allocation of subsidies in a large Italian investment-subsidy program (Law 488/92) as a regression discontinuity design, we estimate that subsidies raised the investment of marginal firms by 39% and their employment by 17% over six years. The gains are heterogeneous: smaller firms grow employment fastest, but larger and younger firms create more jobs per euro of public spending.