Systems with Switching Causal Relations: A Meta-Causal Perspective
Moritz Willig, Tim Nelson Tobiasch, Florian Peter Busch, Jonas Seng,, Devendra Singh Dhami, Kristian Kersting
TL;DR
This paper introduces the concept of meta-causal states to analyze qualitative changes in causal relationships within systems, accounting for dynamic shifts caused by agent actions or environmental tipping points.
Contribution
It proposes a novel framework for identifying and analyzing meta-causal states, which cluster causal models based on qualitative behavior, extending causal analysis to dynamic and context-dependent systems.
Findings
Meta-causal states can be inferred from observed agent behavior.
The framework can disentangle causal states from unlabeled data.
Meta-causal states emerge from system dynamics, not just external factors.
Abstract
Most work on causality in machine learning assumes that causal relationships are driven by a constant underlying process. However, the flexibility of agents' actions or tipping points in the environmental process can change the qualitative dynamics of the system. As a result, new causal relationships may emerge, while existing ones change or disappear, resulting in an altered causal graph. To analyze these qualitative changes on the causal graph, we propose the concept of meta-causal states, which groups classical causal models into clusters based on equivalent qualitative behavior and consolidates specific mechanism parameterizations. We demonstrate how meta-causal states can be inferred from observed agent behavior, and discuss potential methods for disentangling these states from unlabeled data. Finally, we direct our analysis towards the application of a dynamical system, showing…
Peer Reviews
Decision·ICLR 2025 Spotlight
The concept is interesting and the paper is well-written and detailed. Section 4 presents some diverse and interesting applications of the basic theory. The graphics, particularly Figure 1, are clear and easy to understand.
The paper should cite some relevant prior work in graphical models with “gates” (Minka 2008; Winn 2012). The concepts in those papers are somewhat different, but closely related to the topic of this paper. The paper should note that some meta-causal states can actually be represented by ordinary SCMs. For example, in an SCM in which Z is caused by both X and Y, it is entirely possible for Y to control *the manner in which* X affects Z. In the simplest case, Y could be a binary variable that det
The idea of looking for the causes behind an SCM is very interesting and could be relevant in certain contexts. Putting these ideas in practice required the development of a nontrivial algorithm; while generalizing it is left for future work, this does help to solidify the contribution.
- The core concept of the paper seems to be multiply defined. I understand "meta-causal" to refer to questions of what *caused* the structural equations to be what they are (as in line 044-045). But in other places in the paper, it seems to refer to an abstraction where we don't look at the precise structural equations but instead at some qualitative characteristics. E.g. in the dynamical system of stress-induced fatigue in section 4.3, the structural equations remain the same but it is emphasiz
* The manuscript provides a formal framework for handling the dynamics of an SCM. Definitions 1 to 3 are elegant implementations of these ideas. In particular, framing meta-causal models as finite state machines is intuitive and well-suited for applications. * The framework allows for a systematic attributing responsibility in dynamic scenarios. In Section 4.1, the manuscript shows how counterfactual scenarios can be better explained in a meta-causal model. Furthermore, the paper provides a clea
* Meta-causality scope: the paper isn’t clear on two philosophical aspects of the framework regarding its scope and interpretation, namely (i) the causal-effect relationship between the meta-causal model and the causal model instance it is working with and (ii) the potential sutil difference between meta causal edges and classical ones - (i) From the initial example in Figure 1, the reader could question if the meta-causal model is causing the underlying classical causal model. This discussion
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Taxonomy
TopicsComplex Systems and Decision Making