Causal Effects Versus Causal Mechanisms: Two Traditions With Different Requirements and Contributions Towards Causal Understanding

James B. Grace, Nick Huntington-Klein, E. William Schweiger, Melinda Martinez, Michael J. Osland, Laura C. Feher, Glenn R. Guntenspergen, Karen M. Thorne

Research output: Contribution to journalReview articlepeer-review

Abstract

The scientific aspiration of building causal knowledge has received little explicit discussion in ecology despite its fundamental importance. When methods are described as ‘causal’, emphasis is increasingly placed on statistical techniques for isolating associations so as to quantify causal effects. In contrast, natural scientists have historically approached the pursuit of causal knowledge through the investigation of mechanisms that interconnect the components of systems. In this paper, we first summarise a recently published multievidence paradigm for causal studies meant to reconcile conflicting viewpoints. We then describe some of the basic principles of causal statistics and the challenge of estimating pure causal effects. We follow that by describing basic principles related to causal mechanistic investigations, which focus on characterising the structures and processes conveying causal effects. While causal statistics focuses on estimating effect sizes, mechanistic investigations focus on characterising the attributes of the underlying structures and processes linking causative agents to responses. There are important differences between how one approaches each endeavour, as well as differences in what is obtained from each type of investigation. Finally, the case is made that an explicit assessment of existing mechanistic knowledge should be an initial step in causal investigations.

Original languageEnglish (US)
Article numbere70029
JournalEcology letters
Volume28
Issue number4
DOIs
StatePublished - Apr 2025

Bibliographical note

Publisher Copyright:
© 2025 The Author(s). Ecology Letters published by John Wiley & Sons Ltd. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Keywords

  • causal knowledge
  • counterfactual methods
  • extreme events
  • forests
  • mangroves
  • quasi-experimental designs
  • wildfire

PubMed: MeSH publication types

  • Journal Article
  • Review

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