Cover Image

Enabling Decision-Making with the Modified Causal Forest: Policy Trees for Treatment Assignment

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bodory, Hugo, Mascolo, Federica, Lechner, Michael
Format:	Preprint
Published:	2024
Subjects:	Econometrics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Comprehensive Causal Machine Learning
by: Lechner, Michael, et al.
Published: (2024)

Causal Machine Learning for Moderation Effects
by: Bearth, Nora, et al.
Published: (2024)

Causal Identification under Interference: The Role of Treatment Assignment Independence
by: Owusu, Julius, et al.
Published: (2026)

Fairness-Aware and Interpretable Policy Learning
by: Bearth, Nora, et al.
Published: (2025)

Inequality Sensitive Optimal Treatment Assignment
by: Zambrano, Eduardo
Published: (2024)

Causal Inference for Spatial Treatments
by: Pollmann, Michael
Published: (2020)

Estimation of Optimal Dynamic Treatment Assignment Rules under Policy Constraints
by: Sakaguchi, Shosei
Published: (2021)

Inference on Welfare and Value Functionals under Optimal Treatment Assignment
by: Chen, Xiaohong, et al.
Published: (2025)

Robust Inference for the Direct Average Treatment Effect with Treatment Assignment Interference
by: Cattaneo, Matias D., et al.
Published: (2025)

Causal-Policy Forest for End-to-End Policy Learning
by: Kato, Masahiro
Published: (2025)

Budget-Constrained Causal Bandits: Bridging Uplift Modeling and Sequential Decision-Making
by: Pillai, Abhirami
Published: (2026)

Causal Inference for Aggregated Treatment
by: Caetano, Carolina, et al.
Published: (2025)

Dynamic Decision-Making under Model Misspecification
by: Dai, Xinyu
Published: (2025)

Policy Learning with New Treatments
by: Higbee, Samuel
Published: (2022)

Decision Theory for Treatment Choice Problems with Partial Identification
by: Olea, José Luis Montiel, et al.
Published: (2023)

Nonlinearity in Dynamic Causal Effects: Making the Bad into the Good, and the Good into the Great?
by: Kitagawa, Toru, et al.
Published: (2025)

causalfe: Causal Forests with Fixed Effects in Python
by: Aytug, Harry
Published: (2026)

How do applied researchers use the Causal Forest? A methodological review of a method
by: Rehill, Patrick
Published: (2024)

Optimizing Patient Placement in Normal Care Units: An Instrumental Causal Forest Approach Minimizing Mortality
by: Cordier, Johannes
Published: (2026)

Heterogeneous Treatment Effects and Causal Mechanisms
by: Fu, Jiawei, et al.
Published: (2024)

Data-Driven Policy Learning for Continuous Treatments
by: Ai, Chunrong, et al.
Published: (2024)

Semiparametric Efficiency in Policy Learning with General Treatments
by: Fang, Yue, et al.
Published: (2025)

Coping with Inductive Risk When Theories are Underdetermined: Decision Making with Partial Identification
by: Manski, Charles F.
Published: (2026)

Making Event Study Plots Honest: A Functional Data Approach to Causal Inference
by: Fang, Chencheng, et al.
Published: (2025)

Policy Relevant Treatment Effects with Multidimensional Unobserved Heterogeneity
by: Ura, Takuya, et al.
Published: (2024)

When Do Treatment Changes Identify Causal Effects?
by: Huber, Martin
Published: (2026)

Limit Regret in Binary Treatment Choice with Misspecified Plug-In Predictors and Decision Thresholds
by: Dominitz, Jeff, et al.
Published: (2025)

Identifying Causal Effects of Discrete, Ordered and ContinuousTreatments using Multiple Instrumental Variables
by: Hoff, Nadja van 't
Published: (2023)

Doubly Robust Identification of Causal Effects of a Continuous Treatment using Discrete Instruments
by: Dong, Yingying, et al.
Published: (2023)

Difference‐in‐Difference Causal Forests With an Application to Payroll Tax Incidence in Norway
by: Evelina Gavrilova, et al.
Published: (2025)

Estimating Causal Effects of Discrete and Continuous Treatments with Binary Instruments
by: Chernozhukov, Victor, et al.
Published: (2024)

Causal Graphs for Conditional Parallel Trends
by: Knaus, Michael C., et al.
Published: (2026)

Optimal Policy Learning for Multi-Action Treatment with Risk Preference using Stata
by: Cerulli, Giovanni
Published: (2025)

Causal Effect Estimation with Latent Textual Treatments
by: Feldman, Omri, et al.
Published: (2026)

Heterogeneous Responses to Continuous Treatments: A Cluster-Based Causal Framework
by: Cerqua, Augusto, et al.
Published: (2024)

A Decision Theoretic Perspective on Artificial Superintelligence: Coping with Missing Data Problems in Prediction and Treatment Choice
by: Dominitz, Jeff, et al.
Published: (2025)

Identification and Estimation of Unconditional Policy Effects of an Endogenous Binary Treatment: An Unconditional MTE Approach
by: Martinez-Iriarte, Julian, et al.
Published: (2020)

Ordered Correlation Forest
by: Di Francesco, Riccardo
Published: (2023)

Robust and Efficient Estimation of Potential Outcome Means under Random Assignment
by: Negi, Akanksha, et al.
Published: (2020)

Incorporating Cognitive Biases into Reinforcement Learning for Financial Decision-Making
by: He, Liu
Published: (2026)