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causality

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Model Perspective
Model Perspective
Dec 1, 2023 · Artificial Intelligence

Why Causal Graphs Matter: From Philosophy to AI Insights

This article explores the distinction between causal reasoning and conspiracy thinking, the challenges of defining causality, and how Judea Pearl's causal graph framework provides a powerful tool for AI, epidemiology, and other fields to visualize and analyze complex cause‑effect relationships.

Judea Pearlartificial intelligencecausal graphs
0 likes · 10 min read
Why Causal Graphs Matter: From Philosophy to AI Insights
Model Perspective
Model Perspective
Sep 17, 2023 · Fundamentals

Why Correlation Isn’t Causation: Methods to Reveal True Relationships in Data

This article explains the difference between correlation and causation, illustrates common misconceptions with real‑world examples, and introduces statistical tools such as randomized experiments, instrumental variables, propensity score matching, and difference‑in‑differences that help researchers uncover genuine causal effects in mathematical modeling.

causal inferencecausalitycorrelation
0 likes · 9 min read
Why Correlation Isn’t Causation: Methods to Reveal True Relationships in Data
NetEase LeiHuo UX Big Data Technology
NetEase LeiHuo UX Big Data Technology
May 23, 2022 · Fundamentals

Understanding Causality: Philosophical Foundations, Causal Networks, and Simpson’s Paradox

The article explores the concept of causality from philosophical definitions and INUS conditions to statistical approaches like Granger causality, introduces causal network structures (chain, fork, collider), and demonstrates their use in resolving Simpson’s paradox through epidemiological and medical examples.

causal inferencecausal networkscausality
0 likes · 12 min read
Understanding Causality: Philosophical Foundations, Causal Networks, and Simpson’s Paradox
DataFunSummit
DataFunSummit
Mar 16, 2021 · Artificial Intelligence

Myths and Misconceptions in Reinforcement Learning – Summary of Csaba Szepesvári’s KDD 2020 Deep Learning Day Talk

This article summarizes Csaba Szepesvári’s 2020 KDD Deep Learning Day presentation on common myths and misconceptions in reinforcement learning, covering the scope of RL, safety concerns, generalization challenges, causal reasoning, and broader meta‑considerations for the field.

GeneralizationMisconceptionsMyths
0 likes · 16 min read
Myths and Misconceptions in Reinforcement Learning – Summary of Csaba Szepesvári’s KDD 2020 Deep Learning Day Talk
Alibaba Cloud Infrastructure
Alibaba Cloud Infrastructure
Feb 14, 2016 · Big Data

Small Data vs. Big Data: How Minor Signals Guide Robust Data Management

The article explains why small data are essential for avoiding common big‑data mining traps, illustrates pitfalls through real‑world examples, and offers practical methods—incremental improvement, analogical reasoning, and simple modeling—to harness weak signals for more reliable decision‑making.

Bayes theoremBig DataData Mining
0 likes · 11 min read
Small Data vs. Big Data: How Minor Signals Guide Robust Data Management
We-Design
We-Design
Aug 13, 2015 · Fundamentals

Is Time a Fundamental Substance? Exploring Black Holes and Causal Theories

The article philosophically examines whether time is a basic material of the universe by discussing cellular clocks, Planck time, black holes, deterministic causality, and Rafael Sorkin's causal‑set theory, highlighting paradoxes that arise when treating time as a fundamental substance.

black holescausalityphilosophy
0 likes · 7 min read
Is Time a Fundamental Substance? Exploring Black Holes and Causal Theories