Saved in:
| Main Authors: | Hong, Zitao, Peng, Zhen, Liu, Xueping |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.00775 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
A Projection-Based ARIMA Framework for Nonlinear Dynamics in Macroeconomic and Financial Time Series: Closed-Form Estimation and Rolling-Window Inference
by: Liu, Haojie, et al.
Published: (2025)
by: Liu, Haojie, et al.
Published: (2025)
Identifying Elasticities in Autocorrelated Time Series Using Causal Graphs
by: Tiedemann, Silvana, et al.
Published: (2024)
by: Tiedemann, Silvana, et al.
Published: (2024)
Multi-Band Variable-Lag Granger Causality: A Unified Framework for Causal Time Series Inference across Frequencies
by: Sookkongwaree, Chakattrai, et al.
Published: (2025)
by: Sookkongwaree, Chakattrai, et al.
Published: (2025)
Estimation and Inference for Causal Functions with Multiway Clustered Data
by: Liu, Nan, et al.
Published: (2024)
by: Liu, Nan, et al.
Published: (2024)
Forecasting Algorithms for Causal Inference with Panel Data
by: Goldin, Jacob, et al.
Published: (2022)
by: Goldin, Jacob, et al.
Published: (2022)
Regularizing Extrapolation in Causal Inference
by: Arbour, David, et al.
Published: (2025)
by: Arbour, David, et al.
Published: (2025)
Neighborhood Adaptive Estimators for Causal Inference under Network Interference
by: Belloni, Alexandre, et al.
Published: (2022)
by: Belloni, Alexandre, et al.
Published: (2022)
Double Machine Learning for Causal Inference under Shared-State Interference
by: Hays, Chris, et al.
Published: (2025)
by: Hays, Chris, et al.
Published: (2025)
Applied Causal Inference Powered by ML and AI
by: Chernozhukov, Victor, et al.
Published: (2024)
by: Chernozhukov, Victor, et al.
Published: (2024)
CATS: Clustering-Aggregated and Time Series for Business Customer Purchase Intention Prediction
by: Kuang, Yingjie, et al.
Published: (2025)
by: Kuang, Yingjie, et al.
Published: (2025)
Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study
by: Bach, Philipp, et al.
Published: (2024)
by: Bach, Philipp, et al.
Published: (2024)
A Causal Inference Framework for Data Rich Environments
by: Abadie, Alberto, et al.
Published: (2025)
by: Abadie, Alberto, et al.
Published: (2025)
Doubly Robust Inference in Causal Latent Factor Models
by: Abadie, Alberto, et al.
Published: (2024)
by: Abadie, Alberto, et al.
Published: (2024)
Synthetic Combinations: A Causal Inference Framework for Combinatorial Interventions
by: Agarwal, Abhineet, et al.
Published: (2023)
by: Agarwal, Abhineet, et al.
Published: (2023)
Statistical Inference of Optimal Allocations I: Regularities and their Implications
by: Feng, Kai, et al.
Published: (2024)
by: Feng, Kai, et al.
Published: (2024)
Online Causal Inference for Advertising in Real-Time Bidding Auctions
by: Waisman, Caio, et al.
Published: (2019)
by: Waisman, Caio, et al.
Published: (2019)
Semi-Supervised Treatment Effect Estimation with Unlabeled Covariates for Prediction-Powered Causal Inference
by: Kato, Masahiro
Published: (2025)
by: Kato, Masahiro
Published: (2025)
Long-term Causal Inference Under Persistent Confounding via Data Combination
by: Imbens, Guido, et al.
Published: (2022)
by: Imbens, Guido, et al.
Published: (2022)
A Cautionary Tale on Integrating Studies with Disparate Outcome Measures for Causal Inference
by: Parikh, Harsh, et al.
Published: (2025)
by: Parikh, Harsh, et al.
Published: (2025)
Cross-Validated Causal Inference: a Modern Method to Combine Experimental and Observational Data
by: Yang, Xuelin, et al.
Published: (2025)
by: Yang, Xuelin, et al.
Published: (2025)
SPORTSCausal: Spill-Over Time Series Causal Inference
by: Liu, Carol
Published: (2024)
by: Liu, Carol
Published: (2024)
LASSO Inference for High Dimensional Predictive Regressions
by: Gao, Zhan, et al.
Published: (2024)
by: Gao, Zhan, et al.
Published: (2024)
Inference for an Algorithmic Fairness-Accuracy Frontier
by: Liu, Yiqi, et al.
Published: (2024)
by: Liu, Yiqi, et al.
Published: (2024)
Causal Inference on Outcomes Learned from Text
by: Modarressi, Iman, et al.
Published: (2025)
by: Modarressi, Iman, et al.
Published: (2025)
A Gentle Introduction to Conformal Time Series Forecasting
by: Stocker, M., et al.
Published: (2025)
by: Stocker, M., et al.
Published: (2025)
Robust Time Series Causal Discovery for Agent-Based Model Validation
by: Yu, Gene, et al.
Published: (2024)
by: Yu, Gene, et al.
Published: (2024)
The Local Approach to Causal Inference under Network Interference
by: Auerbach, Eric, et al.
Published: (2021)
by: Auerbach, Eric, et al.
Published: (2021)
Econometric vs. Causal Structure-Learning for Time-Series Policy Decisions: Evidence from the UK COVID-19 Policies
by: Petrungaro, Bruno, et al.
Published: (2026)
by: Petrungaro, Bruno, et al.
Published: (2026)
Causal Multi-Task Demand Learning
by: Gupta, Varun, et al.
Published: (2026)
by: Gupta, Varun, et al.
Published: (2026)
Causal Machine Learning for Moderation Effects
by: Bearth, Nora, et al.
Published: (2024)
by: Bearth, Nora, et al.
Published: (2024)
causalfe: Causal Forests with Fixed Effects in Python
by: Aytug, Harry
Published: (2026)
by: Aytug, Harry
Published: (2026)
Anytime-Valid Inference for Double/Debiased Machine Learning of Causal Parameters
by: Dalal, Abhinandan, et al.
Published: (2024)
by: Dalal, Abhinandan, et al.
Published: (2024)
PPI-SVRG: Unifying Prediction-Powered Inference and Variance Reduction for Semi-Supervised Optimization
by: Ao, Ruicheng, et al.
Published: (2026)
by: Ao, Ruicheng, et al.
Published: (2026)
Spatially Robust Inference with Predicted and Missing at Random Labels
by: Salerno, Stephen, et al.
Published: (2026)
by: Salerno, Stephen, et al.
Published: (2026)
Double Machine Learning at Scale to Predict Causal Impact of Customer Actions
by: More, Sushant, et al.
Published: (2024)
by: More, Sushant, et al.
Published: (2024)
Post Reinforcement Learning Inference
by: Syrgkanis, Vasilis, et al.
Published: (2023)
by: Syrgkanis, Vasilis, et al.
Published: (2023)
Re-examining Granger Causality with Causal Bayesian Networks and Reichenbachs Principles
by: Adedayo, S. A.
Published: (2025)
by: Adedayo, S. A.
Published: (2025)
Testing Full Mediation of Treatment Effects and the Identifiability of Causal Mechanisms
by: Huber, Martin, et al.
Published: (2026)
by: Huber, Martin, et al.
Published: (2026)
Causal Diffusion Models for Counterfactual Outcome Distributions in Longitudinal Data
by: Alinezhad, Farbod, et al.
Published: (2026)
by: Alinezhad, Farbod, et al.
Published: (2026)
Bayesian Semiparametric Causal Inference: Targeted Doubly Robust Estimation of Treatment Effects
by: Sert, Gözde, et al.
Published: (2025)
by: Sert, Gözde, et al.
Published: (2025)
Similar Items
-
A Projection-Based ARIMA Framework for Nonlinear Dynamics in Macroeconomic and Financial Time Series: Closed-Form Estimation and Rolling-Window Inference
by: Liu, Haojie, et al.
Published: (2025) -
Identifying Elasticities in Autocorrelated Time Series Using Causal Graphs
by: Tiedemann, Silvana, et al.
Published: (2024) -
Multi-Band Variable-Lag Granger Causality: A Unified Framework for Causal Time Series Inference across Frequencies
by: Sookkongwaree, Chakattrai, et al.
Published: (2025) -
Estimation and Inference for Causal Functions with Multiway Clustered Data
by: Liu, Nan, et al.
Published: (2024) -
Forecasting Algorithms for Causal Inference with Panel Data
by: Goldin, Jacob, et al.
Published: (2022)