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.

Causation without decisions

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]

Causation as a reasoning system

refs [5]

Causation and dispositions

refs [6] [7]


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.
feuilleton/causation_and_decisions.txt · Last modified: 2018/09/12 13:25 by leonb

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