1 views collected around this technical thread.
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.
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.
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.
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.
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.
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.