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Part I
The limits of empiricism.

Part II
The gap

Part III
Causes and effects

Part IV
Causal intuitions

# Causation without decisions

The foundational texts on statistical causation[1][2] frequently remind us that causal inference is necessary to estimate the outcomes of decisions. Suppose for instance that we have access to a trove of medical reports describing the outcomes $Z$ of applying medical treatment $Y$ to patients presenting symptom $X$. It is well known that the conditional distribution $P(Z|X,Y)$ estimated using such a dataset is a poor way to determine which treatment works best. More precisely, this approach fails badly when there exist so called confounding variables $U$ that

1. were not included in the report, but
2. have affected the treatment choices made by the doctors, and
3. have an impact on the outcomes.

# Three views on causation

Although causation is a crucial component of the human cognitive experience, giving a precise and complete definition of causation has proven surprisingly challenging. The purpose of this page is to outline three very different viewpoints on causation that I believe relevant for Artificial Intelligence and inadequately addressed by Machine Learning techniques.

## Causation and manipulation

The manipulative theory of causation has been widely adopted in the statistical community because it accounts for many subtleties one encounters in the interpretation of experimental data [3][2]

refs [4]

refs [5]

refs [6] [7]

## Discussion

refs [8] [9] [10]

[2] Guido W. Imbens, Donald B. Rubin, 2015. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge University Press.
[3] Judea Pearl, 2009. Causality. New York: Cambridge University Press.
[4] Léon Bottou, Jonas Peters, Joaquin Quiñonero-Candela, Denis X. Charles, D. Max Chickering, Elon Portugaly, Dipankar Ray, Patrice Simard, Ed Snelson, Nov 2013. Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising. Journal of Machine Learning Research, 14, pp.3207-3260.
[5] David K. Lewis, 1973. Counterfactuals. Harvard University Press.
[6] Stephen Mumford, Rani Lill Anjum, 2011. Getting Causes from Powers. OUP Oxford.
[7] James J Gibson, 1977. The Theory of Affordances. The Ecological Approach to Visual Perception, Taylor & Francis.
[8] Peter Spirtes, Clark Glymour, Richard Scheines, 2001. Causation, Prediction, and Search, 2nd edition. Cambridge, MA: MIT Press.
[9] David Lopez-Paz, Robert Nishihara, Soumith Chintala, Bernhard Schölkopf, Léon Bottou. Discovering causal signals in images.
[10] Krzysztof Chalupka, Frederik Eberhardt, Pietro Perona, 2017. Causal feature learning: an overview. Behaviormetrika, 44, pp.137-164.