by Issa Kohler-Hausmann (Senior Law and Society Fellow, Spring 2022, Simons Institute)1
This work was made possible by the Simons Institute’s Causality program in the spring of 2022, where I was the Law and Society fellow and had the opportunity to learn and discuss with a collection of brilliant scholars thinking about and working on causality and causal modeling. Special gratitude goes to Robin Dembroff, Maegan Fairchild, and Shamik Dasgupta, who participated in the April 2022 Theoretically Speaking event “Noncausal Dependence and Why It Matters for Causal Reasoning.”
The term “mechanism” or “causal mechanism” is used in two possibly conflicting ways in causal inference literature. Sometimes “causal mechanism” is used to refer to the chain of causal relations that is unleashed between some stipulated triggering event (let’s call it X) and some outcome of interest (let’s call it Y). When people use the term in this sense, they mean “a causal process through which the effect of a treatment on an outcome comes about.”2 One could think of this use of the term as slowing down a movie about the causal process between the moment when X is unleashed and when Y obtains so that we can see more distinct frames capturing ever-finer-grained descriptions of prior events triggering subsequent events as they unfold over time. This is the in-between sense of “mechanism,” or, as Craver says, “causal betweenness.”3 An expansive methodological literature engages causal mechanisms in the in-between sense under the banner of mediation or indirect effects.4 When used in this way, a causal mechanism M lies in the middle of a causal pathway between X and Y: X→M→Y.
But there is a different sense of “mechanism” that refers to whatever it is about the triggering variable (let’s call it X again) that endows it with the causal powers it has. When people use the term in this inside sense, they mean to pick out the constituents of X, the parts and relations that compose it, or the grounds by virtue of which it obtains. Instead of slowing down the movie of a causal process unfolding over time, this use of “mechanism” calls for zooming into X at a particular slice in time.5
Causal models encode mechanisms in the inside sense insofar as denoting a variable (e.g., X) in the model entails denoting the stuff that builds the innards of X in the model.6 Designating variables expresses how the modeler has chosen to carve up states or events in the world. It entails expressing the boundaries of the relata (represented by variables) in the model, as variables marked out as, for example, X and M are taken to be distinct.7 But variable definition often leaves the innards of each relata designated by a variable name — what’s inside of X and M — opaque. And because most causal models we work with are not expressed in terms of fundamental entities (whatever those are — quarks and leptons, or something), variables are built out of or constituted by other things and connections between those things. The variables take the various states designated in the model because certain facts obtain. Inside causal mechanisms are the intravariable relata and relations that compose the variables and give them their distinctive causal powers.
Questions about mechanisms could be posed in one or the other sense of the term. For example, imagine you have a pile of pills and know with absolute certainty that each pill contains the identical chemical substance and dosage. Now imagine you conduct a randomized controlled trial with these pills to see whether ingesting these pills reduces reported headaches, and you document some average causal effect. Upon completion of the study, you might say: “We still do not know the causal mechanisms involved here.” There are simply two meanings to that query.
One meaning is that you do not know what physical processes in the body ingestion of the pill triggered — what physiological pathways ingestion of the substance brought about and unfolded over time such that headache pain was reduced. This version of the query asks about causal mechanisms in the in-between sense. Alternatively, you could mean that you do not know what was in the pill! That is, you have no idea what stuff did the triggering — you do not know the chemical compound that constituted the little pills you gave to your treated subjects.8 This version of the query asks about causal mechanisms in the inside sense, asking what facts obtained such that the thing designated as the cause occurred.
Sometimes people blur these two uses together.9 However, it is important to maintain this conceptual distinction because the relationship between mechanisms in the inside and in-between senses sets some limits on variable definition within a causal model. Specifically, if you posit some mechanism in the in-between sense in a causal model, then the state or event picked out by that mediator cannot be inside another variable.Continue reading