Cover Image

Data Fusion for Partial Identification of Causal Effects

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Lanners, Quinn, Rudin, Cynthia, Volfovsky, Alexander, Parikh, Harsh
Format:	Preprint
Published:	2025
Subjects:	Methodology Machine Learning Econometrics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Double Machine Learning Approach to Combining Experimental and Observational Data
by: Parikh, Harsh, et al.
Published: (2023)

TEA-Time: Transporting Effects Across Time
by: Parikh, Harsh, et al.
Published: (2026)

Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data
by: Katta, Srikar, et al.
Published: (2023)

Safe and Interpretable Estimation of Optimal Treatment Regimes
by: Parikh, Harsh, et al.
Published: (2023)

Regularizing Extrapolation in Causal Inference
by: Arbour, David, et al.
Published: (2025)

A Cautionary Tale on Integrating Studies with Disparate Outcome Measures for Causal Inference
by: Parikh, Harsh, et al.
Published: (2025)

Towards Generalizing Inferences from Trials to Target Populations
by: Huang, Melody Y, et al.
Published: (2024)

Partial Identification under Missing Data Using Weak Shadow Variables from Pretrained Models
by: Chen, Hongyu, et al.
Published: (2026)

Combining Experimental and Observational Data for Identification and Estimation of Long-Term Causal Effects
by: Ghassami, AmirEmad, et al.
Published: (2022)

Effect Identification and Unit Categorization in the Multi-Score Regression Discontinuity Design with Application to LED Manufacturing
by: Schwarz, Philipp Alexander, et al.
Published: (2025)

Identification and Debiased Learning of Causal Effects with General Instrumental Variables
by: Chen, Shuyuan, et al.
Published: (2025)

A Causal Inference Framework for Data Rich Environments
by: Abadie, Alberto, et al.
Published: (2025)

Estimation and Inference for Causal Functions with Multiway Clustered Data
by: Liu, Nan, et al.
Published: (2024)

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

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

Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models
by: Zhou, Guanhao, et al.
Published: (2025)

Neighborhood Adaptive Estimators for Causal Inference under Network Interference
by: Belloni, Alexandre, et al.
Published: (2022)

Long-term Causal Inference Under Persistent Confounding via Data Combination
by: Imbens, Guido, et al.
Published: (2022)

Cross-Validated Causal Inference: a Modern Method to Combine Experimental and Observational Data
by: Yang, Xuelin, et al.
Published: (2025)

Re-examining Granger Causality with Causal Bayesian Networks and Reichenbachs Principles
by: Adedayo, S. A.
Published: (2025)

Multiply-Robust Causal Change Attribution
by: Quintas-Martinez, Victor, et al.
Published: (2024)

Synthetic Potential Outcomes and Causal Mixture Identifiability
by: Mazaheri, Bijan, et al.
Published: (2024)

Applied Causal Inference Powered by ML and AI
by: Chernozhukov, Victor, et al.
Published: (2024)

HMM-LSTM Fusion Model for Economic Forecasting
by: Sivakumar, Guhan
Published: (2025)

Doubly Robust Inference in Causal Latent Factor Models
by: Abadie, Alberto, et al.
Published: (2024)

Synthetic Combinations: A Causal Inference Framework for Combinatorial Interventions
by: Agarwal, Abhineet, et al.
Published: (2023)

Identifying Elasticities in Autocorrelated Time Series Using Causal Graphs
by: Tiedemann, Silvana, et al.
Published: (2024)

Identification and estimation for matrix time series CP-factor models
by: Chang, Jinyuan, et al.
Published: (2024)

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

Partial Identification of Causal Effects for Endogenous Continuous Treatments
by: Dalal, Abhinandan, 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)

Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewal
by: Athey, Susan, et al.
Published: (2023)

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)

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

Mining Causality: AI-Assisted Search for Instrumental Variables
by: Han, Sukjin
Published: (2024)

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

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

Linear Multidimensional Regression with Interactive Fixed-Effects
by: Freeman, Hugo
Published: (2022)

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

Learning from Double Positive and Unlabeled Data for Potential-Customer Identification
by: Kato, Masahiro, et al.
Published: (2025)