====== Causation without decisions ======

The foundational texts on statistical causation[(bibtex:Pearl2002)][(bibtex:ImbensRubin2015)] 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|confounding variables $U$]] that
  - were not included in the report, but
  - have affected the treatment choices made by the doctors, and
  - have an impact on the outcomes. 






==== Causation without decisions ====


{{ causalcones.png?500 }}










====== 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 [(bibtex:Pearl2009)][(bibtex:ImbensRubin2015)]


{{ 3caus-umbrellas.png?150 }}

refs
[(bibtex:Bottou2013)]




===== Causation as a reasoning system =====

refs 
[(bibtex:Lewis1973)]

{{ 3caus-gears.png?250 }}

===== Causation and dispositions =====


refs
[(bibtex:MumfordAnjum2011)]
[(bibtex:Gibson1977)]


{{ 3caus-affordances.png?220 }}


===== Discussion =====

refs
[(bibtex:Spirtes2001)]
[(bibtex:LopezPaz2017)]
[(bibtex:Chalupka2017)]