Cover Image

TEA-Time: Transporting Effects Across Time

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Parikh, Harsh, Levin-Konigsberg, Gabriel, Perrault-Joncas, Dominique, Volfovsky, Alexander
Format:	Preprint
Published:	2026
Subjects:	Methodology Machine Learning Econometrics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Mind the Sim-to-Real Gap & Think Like a Scientist
by: Parikh, Harsh, et al.
Published: (2026)

Data Fusion for Partial Identification of Causal Effects
by: Lanners, Quinn, et al.
Published: (2025)

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

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

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)

Sparse Asymptotic PCA: Identifying Sparse Latent Factors Across Time Horizon in High-Dimensional Time Series
by: Gao, Zhaoxing
Published: (2024)

A Gentle Introduction to Conformal Time Series Forecasting
by: Stocker, M., et al.
Published: (2025)

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

Sequential Kernel Embedding for Mediated and Time-Varying Dose Response Curves
by: Singh, Rahul, et al.
Published: (2021)

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)

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

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

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)

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

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

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)

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

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

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)

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)

Estimating Treatment Effects under Algorithmic Interference: A Structured Neural Networks Approach
by: Zhan, Ruohan, et al.
Published: (2024)

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

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

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

Econometric vs. Causal Structure-Learning for Time-Series Policy Decisions: Evidence from the UK COVID-19 Policies
by: Petrungaro, Bruno, et al.
Published: (2026)

Multi-Band Variable-Lag Granger Causality: A Unified Framework for Causal Time Series Inference across Frequencies
by: Sookkongwaree, Chakattrai, et al.
Published: (2025)

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

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

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

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

Improved Inference for CSDID Using the Cluster Jackknife
by: Karim, Sunny R., et al.
Published: (2026)

Double Machine Learning for Static Panel Data with Instrumental Variables: New Method and Applications
by: Baiardi, Anna, et al.
Published: (2026)

Data-Automated Policy Learning for Nonlinear Welfare
by: Ai, Chunrong, et al.
Published: (2026)

LGB+: A Macroeconomic Forecasting Road Test
by: Coulombe, Philippe Goulet
Published: (2026)

Do covariates explain why these groups differ? The choice of reference group can reverse conclusions in the Oaxaca-Blinder decomposition
by: Quintero, Manuel, et al.
Published: (2026)