= Peter Spirtes
{c}
<Peter Spirtes> develops mathematical and computational methods for <causal discovery>. His work asks which causal conclusions can be recovered from statistical data and background knowledge when fully controlled <experiments> are unavailable, and how to turn the corresponding guarantees into practical <algorithms>.<Peter Spirtes by Carnegie Mellon University>[Research: the two goals of the Tetrad project and limitations of its assumptions]{ref}
With <Clark Glymour> and <Richard Scheines>, he coauthored <Causation, Prediction, and Search by Peter Spirtes, Clark Glymour and Richard Scheines>. One important example is the <PC algorithm>, which uses <conditional independence> information to remove candidate edges and orient some of the remaining ones. Under its assumptions, the resulting graph represents an equivalence class of possible causal explanations; it need not recover every arrow in a unique graph. Making these limitations explicit is part of the method's contribution, rather than an exception to it.<Causation, Prediction, and Search by Peter Spirtes, Clark Glymour and Richard Scheines>[Chapter 5, section 5.4.2, PC algorithm; Markov equivalence and the stated causal assumptions]{ref}
\Image[https://www.cmu.edu/dietrich/philosophy/images/800x800/peter-spirtes.jpg]
{title=Portrait of <Peter Spirtes>}
{description=Portrait selected from the matching Carnegie Mellon institutional profile.}
{source=https://www.cmu.edu/dietrich/philosophy/tetrad/about/people/spirtes.html}
{width=260}
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