AAAI2020最新「因果推理表示学习」【附122页ppt和最新综述论文】.zip_因果表征学习资源-CSDN文库

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AAAI2020最新「因果推理表示学习」122页ppt.zip （2个子文件）

AAAI-20-Causal-Inference-Tutorial.pdf.pdf 8.65MB

2002.02770.pdf.pdf 1.26MB

Representation Learning for Causal Inference

Sheng Li

1

, Liuyi Yao

2

, Yaliang Li

3

, Jing Gao

2

, Aidong Zhang

4

AAAI 2020 Tutorial

Feb. 8, 2020

1

1

University of Georgia, Athens, GA

2

University at Buffalo, Buffalo, NY

3

Alibaba Group, Bellevue, WA

4

University of Virginia, Charlottesville, VA

About this Tutorial

❏ Schedule

❏ 2:00 PM - 2:30 PM: Background on Causal Inference (Li)

❏ 2:30 PM - 3:00 PM: Classical Causal Inference Methods (Yao)

❏ 3:00 PM - 3:30 PM: Subspace Learning for Causal Inference (Li)

❏ 3:30 PM - 3:45 PM: Deep Learning for Causal Inference (Yao)

❏ 3:45 PM - 4:15 PM: Coffee Break

❏ 4:15 PM - 4:50 PM: Deep Learning for Causal Inference (Yao)

❏ 4:50 PM - 5:20 PM: Applications and Conclusions (Li)

❏ Website: http://cobweb.cs.uga.edu/~shengli/AAAI20-Causal-Tutorial.html (Slides)

❏ A Survey on Causal Inference (02/2020): https://arxiv.org/abs/2002.02770

2

Outline

❏ Background on Causal Inference

❏ Classical Causal Inference Methods

❏ Subspace Learning for Causal Inference

❏ Deep Representation Learning for Causal Inference

❏ Applications

❏ Conclusions and Future Perspectives

3

Causality

❏ Causality is also referred to as “causation”, or “cause and effect”

❏ Causality has been extensively discussed in many ﬁelds, such as

philosophy, psychology, statistics, political science, economics, education,

and healthcare

4

Figure: http://www.causality.inf.ethz.ch/cause-effect.php

Correlation and Causation

❏ Correlation does not imply causation

❏ The inability to legitimately deduce a cause-and-effect relationship between

two variables solely on the basis of an observed association or correlation

between them (from Wikipedia)

❏ For two correlated events A and B, the possible relations might be: (1) A

causes B, (2) B causes A, (3) A and B are consequences of a common

cause, but do not cause each other, etc.

❏ Example of (3): As ice cream sales increase, the rate of drowning deaths

increases sharply. Therefore, ice cream consumption causes drowning (?)

5

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