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

Tencent Advertising Technology

Aug 13, 2024 · Artificial Intelligence

Strengthened Symbol Binding Makes Large Language Models Reliable Multiple-Choice Selectors

This paper investigates selection bias in large language models for multiple‑choice tasks, proposes metrics to quantify symbol‑content binding, introduces Reweighting Symbol‑Content Binding (RSCB) and Point‑wise Intelligent Feedback (PIF) methods, and demonstrates their effectiveness in reducing bias and improving accuracy, including a real‑world Tencent advertising feature‑evaluation deployment.

Reinforcement LearningSymbol Bindingmultiple choice

0 likes · 16 min read

Strengthened Symbol Binding Makes Large Language Models Reliable Multiple-Choice Selectors

Model Perspective

Sep 16, 2022 · Fundamentals

Why Adding Non‑Confounding Controls Can Boost Causal Estimates (And When They Hurt)

This article explains how adding covariates that are not confounders can reduce outcome variance and improve causal inference, while controlling for variables that only predict treatment may introduce selection bias and inflate estimation error.

causal inferencecontrol variablesobservational study

0 likes · 21 min read

Why Adding Non‑Confounding Controls Can Boost Causal Estimates (And When They Hurt)

Model Perspective

Sep 12, 2022 · Fundamentals

Unlocking Causal Reasoning: A Beginner’s Guide to Graphical Models

This article introduces causal graphical models as a language for reasoning about cause‑and‑effect, explains key concepts such as conditional independence, colliders, back‑door paths, confounding and selection bias, and shows how to identify and adjust for bias using simple visual examples.

causal inferencecausal reasoningconfounding

0 likes · 17 min read

Unlocking Causal Reasoning: A Beginner’s Guide to Graphical Models