The Causal Foundations of Structural Equation Modeling
On 1 Oct, 2021 By admin 0 Comments
June, 2021
Abstract
Causality
Radical Empiricism and Machine Learning Research
On 23 Aug, 2021 By admin 0 Comments
DSA ADS Course - 2021
Machine Learning, Causality, Causal Models, Knowledge Representation
Discuss “data fitting” vs “data interpreting” approaches to data science along three dimensions: Expediency, Transparency, and Explainability.
Radical Empiricism and Machine Learning Research - May, 2021 by Judea Pearl
Abstract
The Seven Tools of Causal Inference, with Reflections on Machine Learning
On 23 Aug, 2021 By admin 0 Comments
DSA ADS Course - 2021
Causality, Causal Reasoning, Causal Inference, Machine Learning, Algorithms
Discuss causality and machine learning - contrast with statistical correlations.
The Seven Tools of Causal Inference, with Reflections on Machine Learning - March, 2019 By Judea Pearl
The Causal Foundations of Applied Probability and Statistics
On 21 Aug, 2021 By admin 0 Comments
DSA ADS Course - 2021
Probabilistic Logic, Probability Theory, Causality, Causal Inference, Applied Probability, Statistics
Discuss applied probability theory and causality.
The Causal Foundations of Applied Probability and Statistics - November, 2020
Abstract
Control of Confounding and Reporting of Results in Causal Inference Studies
On 21 Aug, 2021 By admin 0 Comments
What is Causal Inference?
On 20 Aug, 2021 By admin 0 Comments
A Critical View of the Structural Causal Model
On 16 Aug, 2021 By admin 0 Comments
DSA ADS Course - 2021
Causal Inference, Causality, Structural Causal Model, SCM
Discuss causality vs. correlation - causality vs. statistics in machine learning. Review techniques for attempting to find true causality.
In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. In the broadest sense correlation is any statistical association, though it commonly refers to the degree to which a pair of variables are linearly related.
Forecasting for COVID19 has Failed
On 11 Aug, 2021 By admin 0 Comments
June, 2020
DSA ADS Course - 2021
Forecasting, COVID19, John Ioannidis, Public Policy, Health Policy, Causal Inference, Forensic Medicine, Causality, Intuitive Causation, Probabilistic Causation
Forecasting is usually impossible in high causal density environments. Scenario planning with applied probability and adaptation to near real-time data is optimal strategy. Epidemic forecasting is usually a fools errand yet appropriate analysis of experience and historical precedent is helpful.
Review of Causal Inference in Forensic Medicine
On 11 Aug, 2021 By admin 0 Comments
March, 2020
DSA ADS Course - 2021
Causal Inference, Forensic Medicine, Causality, Intuitive Causation, Probabilistic Causation
Causality - intuitive causation vs. probabilistic causation. Probabilistic causation is optimal yet discuss other techniques to seek true causation. Sometimes true causation is impossible to find and discuss optimal strategy under such circumstances.
Abstract
Suffcient Causes: On Oxygen, Matches, and Fires
On 20 Jul, 2021 By admin 0 Comments
DSA ADS Course - 2021
Suffcient Causes: On Oxygen, Matches, and Fires
Probability, Causality, Causal Inference, Counterfactuals, Suffcient Causes, Explanation, Abduction
2019
Abstract
We demonstrate how counterfactuals can be used to compute the probability that one event was/is a sufcient cause of another, and how counterfactuals emerge organically from basic scientifc knowledge, rather than manipulative experiments. We contrast this demonstration with the potential outcome framework and address the distinction between causes and enablers.