
Dominos in motion (Wikipedia) 
Preamble The definition of weighted directed graphs (wDAGs) provides a great opportunity to express causal relationships among given variates. Usually this is expressed as SCMs, Structural Causal Model or in more generally causal model. A given causal model can be expressed as set of simultaneous equations, given a direction for the equality, right to left , meaning $A=B$ implies B causes A to happen $B \to A$ . Then what happens if A is a function of B and C, $A=f(B,C)$, then we say $ B \to A$ and $C \to A$ occurs simultaneously. In this post we discuss this situation that there should be
no simultaneity rule in causal models, regardless of if they are not timeseries models.
Understanding causal models
Basic definition of a causal model follows a functional form with set of equations, realistically with added noise. The models forms a weighted Directed Acyclic Graphs (wDAGs) visually. Here is the mathematical definition due to Pearl
(Causality 2009), we made it a bit more coarser in this definition:
Definition (Causal Model) : Given set of $n$variables $X \in \mathbb{R}^{n}$, two subsets of $X= x_{1} \cup x_{2}$, they can form set of equations $x_{2}=f(x_{1}; \alpha; \epsilon)$, $\alpha$ being the causal effect sizes on $x_{1}$ as causes of $x_{2}$ with some noise $\epsilon$. This corresponds to a $wDAG$ formed among $X$ with weights $\alpha$. So that there is a graph $\mathscr{G}(X, \alpha)$ representing this set of equations, where by equality put direction from right to the left side of the equation.
However this definition does not set any constraints on the values of $\alpha$. Any two or more values of $\alpha$s can be equivalent on the same path within $X$. This implies an interestingly that there would be set of variates simultaneously causes the same thing. It sounds plausible and physically possible to a degree within Plancktime. However, this brings an ambiguity of breaking ties in ordering events.
Perfect Causal Ordering
Given wDAG as a causal model induces causal ordering among all members of $X$. As we defined how this can be achieved in a recent post:
Practical causal ordering. In this context,
perfect causal ordering implies $\alpha$ values within the first order paths to a given end variable are different. Mathematically speaking a definition follows.
Definition (No simultaneity rule) Given all $k$ triplets ($x_{i}, y, \alpha_{i}$), that $x_{i}$ is one of the causes of $y$, and $\alpha_{i}$ causal effect sizes, all $\alpha_{i}$ are different numbers, inducing a perfect causal ordering.
This rule ensures we don't need to break ties randomly as causal ordering is established uniformly.
Conclusion: Importance of no simultaneity
By this definition we ruled out any simultaneous causes. This may sound too restrictive for modelling but this impacts decision making significantly; ranking causes of an outcome will impact how to prioritise the policy in addressing the outcome, i.e., such as medical intervention to prevent first cause. Also, it may not be feasible to intervene simultaneous causes. Hence, establishing primary causes in order is paramount in decision making and execution of any reliable policy.
Further reading
Please cite this article as:
@misc{suezen23nos,
title = {Why should there be no simultaneity rule for causal models?},
howpublished = {\url{https://memosisland.blogspot.com/2023/11/causalmodelsimultaneous.html},
author = {Mehmet Süzen},
year = {2023}
}