How Pearl’s Do-Calculus Transforms Causal Inference for Public Health Policies

Pearl’s do‑calculus provides a mathematical framework to derive intervention effects from causal graphs, enabling researchers to predict how policy changes—such as increased vaccination rates—affect disease incidence, with three core rules guiding causal reasoning, substitution, and counterfactual analysis.

Judea Pearlcausal graphscausal inference

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How Pearl’s Do-Calculus Transforms Causal Inference for Public Health Policies

Meituan Technology Team

Jun 19, 2025 · Fundamentals

How to Evaluate Policies Without Experiments: Synthetic Control, Matching, and Causal Impact Explained

Observational research methods—synthetic control, matching, and Causal Impact—offer powerful alternatives to randomized experiments, enabling businesses like Meituan to assess policy effects despite legal and operational constraints, with detailed principles, applications, advantages, limitations, and practical case studies illustrated throughout the guide.

causal inferencematching methodsobservational study

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How to Evaluate Policies Without Experiments: Synthetic Control, Matching, and Causal Impact Explained

AI Algorithm Path

May 19, 2025 · Artificial Intelligence

Understanding Policy Evaluation and Improvement in Reinforcement Learning

This article explains how to solve Bellman equations, use iterative policy‑evaluation methods, apply the policy‑improvement theorem, and combine both steps in policy iteration, value iteration, and asynchronous variants, illustrated with a 5‑state example and a 4×4 gridworld.

Bellman equationGridWorldgeneralized policy iteration

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Understanding Policy Evaluation and Improvement in Reinforcement Learning

DataFunTalk

May 1, 2023 · Artificial Intelligence

Trustworthy Intelligent Decision-Making: Framework, Counterfactual Reasoning, Complex Payoffs, Predictive Fairness, and Regulated Decisions

This article presents a comprehensive overview of trustworthy intelligent decision-making, introducing a decision framework and discussing counterfactual reasoning, complex reward modeling, predictive fairness, and regulatory constraints, while highlighting practical methods and recent research advances in each sub‑area.

Fairnesscausal inferencecounterfactual reasoning

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Trustworthy Intelligent Decision-Making: Framework, Counterfactual Reasoning, Complex Payoffs, Predictive Fairness, and Regulated Decisions

Model Perspective

Feb 27, 2023 · Fundamentals

Mastering Difference-in-Differences: Theory, Example, and Python Implementation

Learn how the Difference-in-Differences (DiD) method estimates policy impacts by comparing treatment and control groups over time, explore its mathematical model, see a concrete traffic‑restriction example, and follow a step‑by‑step Python implementation with data analysis and visualization.

Pythondifference-in-differenceseconometrics

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Mastering Difference-in-Differences: Theory, Example, and Python Implementation