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Probabilistic Programming and Bayesian Inference for Time Series Analysis and Forecasting in Python
The original article can be found here at Towards Data Science
Probabilistic Programming and Bayesian Inference for Time Series Analysis and Forecasting in Python: A Bayesian Method for Time Series Data Analysis and Forecasting in Python – by Yuefeng...
Efficient Intervention Design for Causal Discovery with Latents
Raghavendra Addanki, Shiva Prasad Kasiviswanathan, Andrew McGregor, Cameron Musco
Professor Judea Pearl wrote:”An interesting paper on causal discovery using both observations and interventions. The problem is to recover a causal graph while...
Simpson’s paradox in Covid-19 case fatality rates: a mediation analysis of age-related causal effects
Julius von Kügelgen, Luigi Gresele, Bernhard Schölkopf
Professor Judea Pearl wrote: “This paper is the first COVID-19 analysis I read that goes beyond data-fitting and tells society: Yes! AI can be trusted to extract meaning from data, and can do...
A Guided Tour of Artificial Intelligence Research
Volume I: Knowledge Representation, Reasoning and Learning
The purpose of this book is to provide an overview of AI research, ranging from basic work to interfaces and applications, with as much emphasis on results as on current issues....
On The Reasons Behind Decisions
Adnan Darwiche, Auguste Hirth
Professor Judea Pearl commented “Speaking about “explainable AI”, this paper shows that, even in classification tasks, and even after agreeing on a Bayesian Network classifier, answering “why”...
Decision-theoretic foundations for statistical causality
A. Philip Dawid
We develop a mathematical and interpretative foundation for the enterprise of decision-theoretic statistical causality (DT), which is a straightforward way of representing and addressing causal questions. DT reframes causal inference as...
Journal of Causal Inference
Outstanding editorial board
One of leading journals in causal inference
Open access publication
Journal of Causal Inference (JCI) is a fully peer-reviewed, open...
Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense
Yixin Zhu, Tao Gao, Lifeng Fan, Siyuan Huang, Mark Edmonds, Hangxin Liu, Feng Gao, Chi Zhang, Siyuan Qi, Ying Nian Wu, Joshua B. Tenenbaum, Song-Chun Zhu
Recent progress in deep learning is essentially based on a “big data for small tasks”...
Causal Inference that’s not A/B Testing: Theory & Practical Guide
WRITTEN BY Eva Gong
Undoubtedly, randomized experimentation (assuming it is conducted properly) is the most straightforward way to establish causality (refer to my previous article on a collection of A/B testing learning resources!). However, practically...