A review of off-policy evaluation in reinforcement learning
Reinforcement learning (RL) is one of the most vibrant research frontiers in machine
learning and has been recently applied to solve a number of challenging problems. In this …
learning and has been recently applied to solve a number of challenging problems. In this …
Causal inference in statistics: An overview
J Pearl - 2009 - projecteuclid.org
This review presents empirical researchers with recent advances in causal inference, and
stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical …
stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical …
On Pearl's hierarchy and the foundations of causal inference
Cause-and-effect relationships play a central role in how we perceive and make sense of
the world around us, how we act upon it, and ultimately, how we under stand ourselves …
the world around us, how we act upon it, and ultimately, how we under stand ourselves …
The seven tools of causal inference, with reflections on machine learning
J Pearl - Communications of the ACM, 2019 - dl.acm.org
The seven tools of causal inference, with reflections on machine learning Page 1 54
COMMUNICATIONS OF THE ACM | MARCH 2019 | VOL. 62 | NO. 3 contributed articles ILL US …
COMMUNICATIONS OF THE ACM | MARCH 2019 | VOL. 62 | NO. 3 contributed articles ILL US …
[图书][B] The book of why: the new science of cause and effect
J Pearl, D Mackenzie - 2018 - books.google.com
A Turing Award-winning computer scientist and statistician shows how understanding
causality has revolutionized science and will revolutionize artificial intelligence" Correlation …
causality has revolutionized science and will revolutionize artificial intelligence" Correlation …
[图书][B] Causal inference in statistics: A primer
CAUSAL INFERENCE IN STATISTICS A Primer Causality is central to the understanding
and use of data. Without an understanding of cause–effect relationships, we cannot use data …
and use of data. Without an understanding of cause–effect relationships, we cannot use data …
Causal inference and the data-fusion problem
E Bareinboim, J Pearl - Proceedings of the National …, 2016 - National Acad Sciences
We review concepts, principles, and tools that unify current approaches to causal analysis
and attend to new challenges presented by big data. In particular, we address the problem …
and attend to new challenges presented by big data. In particular, we address the problem …
Theoretical impediments to machine learning with seven sparks from the causal revolution
J Pearl - arXiv preprint arXiv:1801.04016, 2018 - arxiv.org
Current machine learning systems operate, almost exclusively, in a statistical, or model-free
mode, which entails severe theoretical limits on their power and performance. Such systems …
mode, which entails severe theoretical limits on their power and performance. Such systems …
Fair inference on outcomes
R Nabi, I Shpitser - Proceedings of the AAAI Conference on Artificial …, 2018 - ojs.aaai.org
In this paper, we consider the problem of fair statistical inference involving outcome
variables. Examples include classification and regression problems, and estimating …
variables. Examples include classification and regression problems, and estimating …
The causal-neural connection: Expressiveness, learnability, and inference
One of the central elements of any causal inference is an object called structural causal
model (SCM), which represents a collection of mechanisms and exogenous sources of …
model (SCM), which represents a collection of mechanisms and exogenous sources of …