arXiv subjects

Pedro Picchetti

Publications and source records attributed to Pedro Picchetti.

6 recordsLinked to original sources

arXiv2026

Sensitivity Analysis for Instrumental Variables Under Joint Relaxations of Monotonicity and Independence

In this paper I develop a breakdown frontier approach to assess the sensitivity of Local Average Treatment Effects (LATE) estimates to violations of monotonicity and independence of the instrument. I parametrize violations of independence using the concept of $c$-dependence from Masten & Poirier (2018) and allow for the share of defiers to be greater than zero but smaller than the share of compliers. I derive identified sets for the LATE and the Average Treatment Effect (ATE) in which the bounds are functions of these two sensitivity parameters. Using these bounds, I derive the breakdown frontier for the LATE, which is the weakest set of assumptions such that a conclusion regarding the LATE holds. I derive consistent sample analogue estimators for the breakdown frontiers and provide a valid bootstrap procedure for inference. Monte Carlo simulations show the desirable finite-sample properties of the estimators and an empirical application shows that the conclusions regarding the effect of family size on unemployment from Angrist & Evans (1998) are highly sensitive to violations of independence and monotonicity.

econ.EM↗

arXiv2026

Finite Population Inference for Factorial Designs and Panel Experiments with Imperfect Compliance

This paper develops a finite population framework for analyzing causal effects in settings with imperfect compliance where multiple treatments affect the outcome of interest. Two prominent examples are factorial designs and panel experiments with imperfect compliance. I define finite population causal effects that capture the relative effectiveness of alternative treatment sequences. I provide nonparametric estimators for a rich class of factorial and dynamic causal effects and derive their finite population distributions as the sample size increases. Monte Carlo simulations illustrate the desirable properties of the estimators. Finally, I use the estimator for causal effects in factorial designs to revisit a famous voter mobilization experiment that analyzes the effects of voting encouragement through phone calls on turnout.

stat.ME↗

arXiv2025

Microfoundations and the Causal Interpretation of Price-Exposure Designs

This paper studies regional exposure designs that use commodity prices as instruments to study local effects of aggregate shocks. Unlike standard shift share designs that leverage differential exposure to many shocks, the price exposure relies on exogenous variation from a single shock, leading to challenges for both identification and inference. We motivate the design using a multi sector labor model. Under the model and a potential outcomes framework, we characterize the 2SLS and TWFE estimands as weighted averages of region and sector specific effects plus contamination terms driven by the covariance structure of prices and by general-equilibrium output responses. We derive conditions under which these estimands have a clear causal interpretation and provide simple sensitivity analysis procedures for violations. Finally, we show that standard inference procedures suffer from an overrejection problem in price-exposure designs. We derive a new standard error estimator and show its desirable finite-sample properties through Monte Carlo simulations. In an application to gold mining and homicides in the Amazon, the price exposure standard errors are roughly twice as large as conventional clustered standard errors, making the main effect statistically insignificant.

econ.EM↗

arXiv2025

Breakdown Analysis for Instrumental Variables with Binary Outcomes

This paper studies the partial identification of treatment effects in Instrumental Variables (IV) settings with binary outcomes under violations of independence. I derive the identified sets for the treatment parameters of interest in the setting, as well as breakdown values for conclusions regarding the true treatment effects. I derive $\sqrt{N}$-consistent nonparametric estimators for the bounds of treatment effects and for breakdown values. These results can be used to assess the robustness of empirical conclusions obtained under the assumption that the instrument is independent from potential quantities, which is a pervasive concern in studies that use IV methods with observational data. In the empirical application, I show that the conclusions regarding the effects of family size on female unemployment using same-sex siblings as the instrument are highly sensitive to violations of independence.

econ.EM↗

arXiv2024

Difference-in-Discontinuities: Estimation, Inference and Validity Tests

This paper provides a formal econometric framework behind the newly developed difference-in-discontinuities design (DiDC). Despite its increasing use in applied research, there are currently limited studies of its properties. We formalize the theory behind the difference-in-discontinuity approach by stating the identification assumptions, proposing a nonparametric estimator, and deriving its asymptotic properties. We also provide comprehensive tests for one of the identification assumption of the DiDC and sensitivity analysis methods that allow researchers to evaluate the robustness of DiDC estimates under violations of the identifying assumptions. Monte Carlo simulation studies show that the estimators have desirable finite-sample properties. Finally, we revisit Grembi et al. (2016), which studies the effects of relaxing fiscal rules on public finance outcomes. Our results show that most of the qualitative takeaways of the original work are robust to time-varying confounding effects.

econ.EM↗

arXiv2023

Identification in Endogenous Sequential Treatment Regimes

This paper develops a novel nonparametric identification method for treatment effects in settings where individuals self-select into treatment sequences. I propose an identification strategy which relies on a dynamic version of standard Instrumental Variables (IV) assumptions and builds on a dynamic version of the Marginal Treatment Effects (MTE) as the fundamental building block for treatment effects. The main contribution of the paper is to relax assumptions on the support of the observed variables and on unobservable gains of treatment that are present in the dynamic treatment effects literature. Monte Carlo simulation studies illustrate the desirable finite-sample performance of a sieve estimator for MTEs and Average Treatment Effects (ATEs) on a close-to-application simulation study.

econ.EM↗