How to Build Efficient Causal Effect Estimators for Exponential‑Family Outcomes

This article presents a unified framework for efficiently estimating causal treatment effects on exponential‑family outcomes, extending target regularization beyond Gaussian assumptions, deriving bias analysis for plug‑in estimators, proposing DR and TMLE‑based estimators, and validating them on synthetic and real datasets.

Neural Networkscausal inferenceexponential family

0 likes · 12 min read

How to Build Efficient Causal Effect Estimators for Exponential‑Family Outcomes

Alimama Tech

Sep 11, 2024 · Artificial Intelligence

A Generative Approach for Treatment Effect Estimation under Collider Bias: From an Out-of-Distribution Perspective

The paper introduces a coupled generative adversarial framework that merges biased observational with unbiased experimental data to create a bias‑free dataset for causal inference, enabling robust treatment‑effect estimation under collider bias from an out‑of‑distribution perspective, and demonstrates superior bias reduction on three public advertising datasets.

Generative Adversarial Networkscausal inferenceincremental value

0 likes · 10 min read

A Generative Approach for Treatment Effect Estimation under Collider Bias: From an Out-of-Distribution Perspective

DataFunTalk

Jul 12, 2024 · Artificial Intelligence

Weak Supervision Machine Learning for Ant Group Business Scenarios: Methods, Experiments, and Applications

This article presents a comprehensive overview of weak supervision machine learning techniques applied to Ant Group's business problems, covering theoretical foundations, cross‑domain causal effect estimation, noisy‑label denoising frameworks, experimental results, and practical use cases such as risk modeling and marketing interventions.

Weak Supervisioncausal inferencecross-domain learning

0 likes · 16 min read

Weak Supervision Machine Learning for Ant Group Business Scenarios: Methods, Experiments, and Applications