Hugo Bodory
Last Name
Bodory
First name
Hugo
Email
hugo.bodory@unisg.ch
Phone
+41 71 224 27 24
4 results
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Item type:Publication, Enabling Decision Making with the Modified Causal Forest: Policy Trees for Treatment Assignmen(2024-07-19); ; Federica MascoloDecision making plays a pivotal role in shaping outcomes across various disciplines, such as medicine, economics, and business. This paper provides practitioners with guidance on implementing a decision tree designed to optimise treatment assignment policies through an interpretable and non-parametric algorithm. Building upon the method proposed by Zhou, Athey, and Wager (2023), our policy tree introduces three key innovations: a different approach to policy score calculation, the incorporation of constraints, and enhanced handling of categorical and continuous variables. These innovations enable the evaluation of a broader class of policy rules, all of which can be easily obtained using a single module. We showcase the effectiveness of our policy tree in managing multiple, discrete treatments using datasets from diverse fields. Additionally, the policy tree is implemented in the open-source Python package mcf (modified causal forest), facilitating its application in both randomised and observational research settings.Type:journal article - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The Finite Sample Performance of Inference Methods for Propensity Score Matching and Weighting Estimators(American Statistical Association, 2020-01); ; This article investigates the finite sample properties of a range of inference methods for propensity score-based matching and weighting estimators frequently applied to evaluate the average treatment effect on the treated. We analyze both asymptotic approximations and bootstrap methods for computing variances and confidence intervals in our simulation designs, which are based on German register data and U.S. survey data. We vary the design w.r.t. treatment selectivity, effect heterogeneity, share of treated, and sample size. The results suggest that in general, theoretically justified bootstrap procedures (i.e., wild bootstrapping for pair matching and standard bootstrapping for “smoother” treatment effect estimators) dominate the asymptotic approximations in terms of coverage rates for both matching and weighting estimators. Most findings are robust across simulation designs and estimators.Type:journal articleJournal:Journal of Business & Economic StatisticsVolume:38Issue:1Scopus© Citations 36 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, High Resolution Treatment Effects Estimation: Uncovering Effect Heterogeneities with the Modified Causal ForestThere is great demand for inferring causal effect heterogeneity and for open-source statistical software, which is readily available for practitioners. The mcf package is an open-source Python package that implements Modified Causal Forest (mcf), a causal machine learner. We replicate three well-known studies in the fields of epidemiology, medicine, and labor economics to demonstrate that our mcf package produces aggregate treatment effects, which align with previous results, and in addition, provides novel insights on causal effect heterogeneity. For all resolutions of treatment effects estimation, which can be identified, the mcf package provides inference. We conclude that the mcf constitutes a practical and extensive tool for a modern causal heterogeneous effects analysis.Type:forthcomingJournal:EntropyVolume:24Issue:1039Scopus© Citations 11 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Enabling Decision-Making with the Modified Causal Forest: Policy Trees for Treatment Assignment(2024-06-04); ; Federica MascoloDecision-making plays a pivotal role in shaping outcomes in various disciplines, such as medicine, economics, and business. This paper provides guidance to practitioners on how to implement a decision tree designed to address treatment assignment policies using an interpretable and non-parametric algorithm. Our Policy Tree is motivated on the method proposed by Zhou, Athey, and Wager (2023), distinguishing itself for the policy score calculation, incorporating constraints, and handling categorical and continuous variables. We demonstrate the usage of the Policy Tree for multiple, discrete treatments on data sets from different fields. The Policy Tree is available in Python's open-source package mcf (Modified Causal Forest).Type:working paper