Cover Image

Estimating Treatment Effects under Algorithmic Interference: A Structured Neural Networks Approach

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhan, Ruohan, Han, Shichao, Hu, Yuchen, Jiang, Zhenling
Format:	Preprint
Published:	2024
Subjects:	Econometrics Machine Learning Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Individualized Policy Evaluation and Learning under Clustered Network Interference
by: Zhang, Yi, et al.
Published: (2023)

The Local Approach to Causal Inference under Network Interference
by: Auerbach, Eric, et al.
Published: (2021)

Can We Validate Counterfactual Estimations in the Presence of General Network Interference?
by: Shirani, Sadegh, et al.
Published: (2025)

Off-Policy Evaluation via Adaptive Weighting with Data from Contextual Bandits
by: Zhan, Ruohan, et al.
Published: (2021)

Estimating Dyadic Treatment Effects with Unknown Confounders
by: Hoshino, Tadao, et al.
Published: (2024)

An Algorithm for Identifying Interpretable Subgroups With Elevated Treatment Effects
by: Chiu, Albert
Published: (2025)

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

Active Adaptive Experimental Design for Treatment Effect Estimation with Covariate Choices
by: Kato, Masahiro, et al.
Published: (2024)

Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index
by: Athey, Susan, et al.
Published: (2016)

Synthetic Blips: Generalizing Synthetic Controls for Dynamic Treatment Effects
by: Agarwal, Anish, et al.
Published: (2022)

Tackling Interference Induced by Data Training Loops in A/B Tests: A Weighted Training Approach
by: Si, Nian
Published: (2023)

Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation
by: Jin, Jikai, et al.
Published: (2024)

Educational Effects in Mathematics: Conditional Average Treatment Effect depending on the Number of Treatments
by: Nagai, Tomoko, et al.
Published: (2024)

Policy Targeting under Network Interference
by: Viviano, Davide
Published: (2019)

Improving the Estimation of Lifetime Effects in A/B Testing via Treatment Locality
by: Chen, Shuze, et al.
Published: (2024)

Dynamic Local Average Treatment Effects
by: Sojitra, Ravi B., et al.
Published: (2024)

An Adversarial Approach to Structural Estimation
by: Kaji, Tetsuya, et al.
Published: (2020)

Identification of Average Treatment Effects in Nonparametric Panel Models
by: Athey, Susan, et al.
Published: (2025)

Robust and Agnostic Learning of Conditional Distributional Treatment Effects
by: Kallus, Nathan, et al.
Published: (2022)

Detecting and Mitigating Group Bias in Heterogeneous Treatment Effects
by: Persson, Joel, et al.
Published: (2026)

Double Robust Bayesian Inference on Average Treatment Effects
by: Breunig, Christoph, et al.
Published: (2022)

Direct Bias-Correction Term Estimation for Average Treatment Effect Estimation
by: Kato, Masahiro
Published: (2025)

Estimating Causal Effects with Double Machine Learning -- A Method Evaluation
by: Fuhr, Jonathan, et al.
Published: (2024)

Triple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effects
by: Kato, Masahiro
Published: (2024)

A Large-Scale Empirical Comparison of Meta-Learners and Causal Forests for Heterogeneous Treatment Effect Estimation in Marketing Uplift Modeling
by: Singh, Aman
Published: (2026)

Adaptive Estimation and Uniform Confidence Bands for Nonparametric Structural Functions and Elasticities
by: Chen, Xiaohong, et al.
Published: (2021)

What Makes Treatment Effects Identifiable? Characterizations and Estimators Beyond Unconfoundedness
by: Cai, Yang, et al.
Published: (2025)

Modeling Covariate Transition for Efficient Estimation of Longitudinal Treatment Effects in Randomized Experiments
by: Chihara, Naoki, et al.
Published: (2026)

Bayesian Semiparametric Causal Inference: Targeted Doubly Robust Estimation of Treatment Effects
by: Sert, Gözde, et al.
Published: (2025)

Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks
by: Huch, Easton, et al.
Published: (2026)

xtdml: Double Machine Learning Estimation to Static Panel Data Models with Fixed Effects in R
by: Polselli, Annalivia
Published: (2025)

Semi-Supervised Treatment Effect Estimation with Unlabeled Covariates for Prediction-Powered Causal Inference
by: Kato, Masahiro
Published: (2025)

The Honest Truth About Causal Trees: Accuracy Limits for Heterogeneous Treatment Effect Estimation
by: Cattaneo, Matias D., et al.
Published: (2025)

PUATE: Efficient Average Treatment Effect Estimation from Treated (Positive) and Unlabeled Units
by: Kato, Masahiro, et al.
Published: (2025)

Bridging the Gap between Empirical Welfare Maximization and Conditional Average Treatment Effect Estimation in Policy Learning
by: Kato, Masahiro
Published: (2025)

Shrinkage-Based Regressions with Many Related Treatments
by: Dilber, Enes, et al.
Published: (2025)

Calibration Strategies for Robust Causal Estimation: Theoretical and Empirical Insights on Propensity Score-Based Estimators
by: Klaassen, Sven, et al.
Published: (2025)

Robust Matrix Estimation with Side Information
by: Agarwal, Anish, et al.
Published: (2026)

LASSO Inference for High Dimensional Predictive Regressions
by: Gao, Zhan, et al.
Published: (2024)

Experimental Design under Network Interference
by: Viviano, Davide
Published: (2020)