Peter Spirtes

About

Peter Spirtes is the Marianna Brown Dietrich Professor and Head of Philosophy at the Department of Philosophy within the Dietrich College of Humanities and Social Sciences at Carnegie Mellon University. His research primarily focuses on the inference of causal relationships from statistical data, especially in contexts where fully controlled experiments are not feasible. He leads the TETRAD project, which aims to specify and prove conditions under which reliable causal inferences can be made from background knowledge and observational data, and to develop practical computer programs for inferring causal structures. His interdisciplinary work involves philosophy, statistics, graph theory, and computer science, with significant implications for various disciplines that rely on causal inference from data. Spirtes's research explores the limits of causal inference, the relationship between probability and causality, and the development of tools to assist scientists in building causal models. His contributions include the development of algorithms and software such as the TETRAD II program, and his work has advanced understanding of causal inference, Markov equivalence, and the conditions under which causal conclusions can be reliably drawn from data.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Data Mining
• Mathematics
• Statistics
• Econometrics
• Data science
• Epistemology
• Software engineering

Selected publications

• Causal-learn: Causal Discovery in Python

  arXiv (Cornell University) · 2023 · 26 citations

  Causal discovery aims at revealing causal relations from observational data, which is a fundamental task in science and engineering. We describe $\textit{causal-learn}$, an open-source Python library for causal discovery. This library focuses on bringing a comprehensive collection of causal discovery methods to both practitioners and researchers. It provides easy-to-use APIs for non-specialists, modular building blocks for developers, detailed documentation for learners, and comprehensive method…

  DOI

• Constructing Causal Life-Course Models: Comparative Study of Data-Driven and Theory-Driven Approaches

  American Journal of Epidemiology · 2023 · 20 citations

  Life-course epidemiology relies on specifying complex (causal) models that describe how variables interplay over time. Traditionally, such models have been constructed by perusing existing theory and previous studies. By comparing data-driven and theory-driven models, we investigated whether data-driven causal discovery algorithms can help in this process. We focused on a longitudinal data set on a cohort of Danish men (the Metropolit Study, 1953-2017). The theory-driven models were constructed…

  DOI

• Causal Discovery for Observational Sciences Using Supervised Machine Learning

  Journal of Data Science · 2023 · 7 citations

  Senior authorCorresponding

  Causal inference can estimate causal effects, but unless data are collected experimentally, statistical analyses must rely on pre-specified causal models. Causal discovery algorithms are empirical methods for constructing such causal models from data. Several asymptotically correct discovery methods already exist, but they generally struggle on smaller samples. Moreover, most methods focus on very sparse causal models, which may not always be a realistic representation of real-life data generati…

  DOI

• Prompting Fairness: Integrating Causality to Debias Large Language Models

  arXiv (Cornell University) · 2024-03-13 · 6 citations

  preprintOpen access

  Large language models (LLMs), despite their remarkable capabilities, are susceptible to generating biased and discriminatory responses. As LLMs increasingly influence high-stakes decision-making (e.g., hiring and healthcare), mitigating these biases becomes critical. In this work, we propose a causality-guided debiasing framework to tackle social biases, aiming to reduce the objectionable dependence between LLMs' decisions and the social information in the input. Our framework introduces a novel…

  PublisherOA PDFDOI

• Causal discovery and epidemiology: a potential for synergy

  American Journal of Epidemiology · 2024-05-31 · 5 citations

  articleOpen access

  Introduction<br/>We wish to applaud the insightful commentary provided by Didelez1 on our comparative study of data-driven versus theory-driven approaches for constructing causal life-course models.2 Didelez’ concise and honest description of both possibilities and limitations of causal discovery for epidemiology will be a tremendously useful resource moving forward.<br/><br/>We will address a question posed by Didelez regarding the expert consensus meeting and provide a few additional topics fo…

  PublisherOA PDFDOI

Recent grants

• Algorithms for Measurement Model Specification Search

  NSF · $430k · 2013–2016

• Interpretable graphical models for large multi-modal COPD data (R01HL159805)

  NIH · $2.0M · 2021–2026

Frequent coauthors

• Clark Glymour

  112 shared

• Richard Scheines

  Carnegie Mellon University

  78 shared

• Jiji Zhang

  24 shared

• Joseph Ramsey

  24 shared

• Kevin T. Kelly

  18 shared

• Kun Zhang

  Xiamen University

  16 shared

• Thomas Richardson

  University of Bristol

  13 shared

• Christopher Meek

  11 shared

Similar researchers at Carnegie Mellon University

• Alex John Londonh-39
• Teddy Seidenfeldh-31