Skip to main content

ETHNOS_APP

Home • Search • Journals • List 0

Vítor Possebom

Biographic Data

ID6369804
NAMEVítor Possebom
GIVEN NAMESVítor
FAMILY NAMEPossebom
SIGNATUREPOSSEBOM V
AFFILIATIONSSão Paulo School of Economics–FGV
ORCID0000-0002-2850-3422
VERIFIEDYes
TOTAL WORKS3
TOTAL CITATIONS20
WORKS AS AUTHOR3
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2020
LATEST PUBLICATION YEAR2026
H-INDEX1
  • Crime and Mismeasured Punishment

    Vítor Possebom•ARTICLE•The Review of Economics and…•2026

    I partially identify the marginal treatment effect (MTE) when the treatment is misclassified. I explore two restrictions, allowing for dependence between the instrument and the misclassification decision. If the signs of the propensity scores’ derivatives are equal, I identify the MTE sign. If those derivatives are similar, I bound the MTE. To illustrate, I analyze the impact of alternative sentences (fines and community service versus no punishm…

  • Probability of Causation with Sample Selection

    Vítor Possebom, Flavio Riva•ARTICLE•Journal of Business and Economic…•2025

    This paper identifies the probability of causation when there is sample selection. We show that the probability of causation is partially identified for individuals who are always observed regardless of treatment status and derive sharp bounds under three increasingly restrictive sets of assumptions. The first set imposes an exogenous treatment and a monotone sample selection mechanism. To tighten these bounds, the second set also imposes the mon…

  • Cherry Picking with Synthetic Controls

    Open Access•Bruno Ferman, Cristine Campos De Xavier Pinto et al.•ARTICLE•Journal of Policy Analysis and…•2020•Cited by: 20•References: 5

    We evaluate whether a lack of guidance on how to choose the matching variables used in the Synthetic Control (SC) estimator creates specification‐searching opportunities. We provide theoretical results showing that specification‐searching opportunities are asymptotically irrelevant if we restrict to a subset of SC specifications. However, based on Monte Carlo simulations and simulations with real datasets, we show significant room for specificati…

  • Cherry Picking with Synthetic Controls

    Open Access•Bruno Ferman, Cristine Campos De Xavier Pinto et al.•ARTICLE•Journal of Policy Analysis and…•2020•Cited by: 20•References: 5

    We evaluate whether a lack of guidance on how to choose the matching variables used in the Synthetic Control (SC) estimator creates specification‐searching opportunities. We provide theoretical results showing that specification‐searching opportunities are asymptotically irrelevant if we restrict to a subset of SC specifications. However, based on Monte Carlo simulations and simulations with real datasets, we show significant room for specificati…

  • Cherry Picking with Synthetic Controls

    Open Access•Bruno Ferman, Cristine Campos De Xavier Pinto et al.•ARTICLE•Journal of Policy Analysis and…•2020•Cited by: 20•References: 5

    We evaluate whether a lack of guidance on how to choose the matching variables used in the Synthetic Control (SC) estimator creates specification‐searching opportunities. We provide theoretical results showing that specification‐searching opportunities are asymptotically irrelevant if we restrict to a subset of SC specifications. However, based on Monte Carlo simulations and simulations with real datasets, we show significant room for specificati…

  • Probability of Causation with Sample Selection

    Vítor Possebom, Flavio Riva•ARTICLE•Journal of Business and Economic…•2025

    This paper identifies the probability of causation when there is sample selection. We show that the probability of causation is partially identified for individuals who are always observed regardless of treatment status and derive sharp bounds under three increasingly restrictive sets of assumptions. The first set imposes an exogenous treatment and a monotone sample selection mechanism. To tighten these bounds, the second set also imposes the mon…

  • Crime and Mismeasured Punishment

    Vítor Possebom•ARTICLE•The Review of Economics and…•2026

    I partially identify the marginal treatment effect (MTE) when the treatment is misclassified. I explore two restrictions, allowing for dependence between the instrument and the misclassification decision. If the signs of the propensity scores’ derivatives are equal, I identify the MTE sign. If those derivatives are similar, I bound the MTE. To illustrate, I analyze the impact of alternative sentences (fines and community service versus no punishm…

Advanced Causal Inference Techniques (3 works) · Artificial Intelligence (3 works) · Computer Science (3 works) · Mathematics (2 works) · Psychology (2 works) · Social Psychology (2 works) · Statistical Methods and Inference (2 works) · Statistical Methods in Clinical Trials (2 works) · Statistics (2 works) · Actuarial science (1 work)

Ethnos_APP • Open Source Project • MIT License • Frontend v2.0.0 • Privacy and Cookies • API Documentation: api.ethnos.app/docs • API Source Code: GitHub • DOI: 10.5281/zenodo.17049435 • Frontend Source Code: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae