When Counterfactual Reasoning Fails: Chaos and Real-World Complexity

• URL: http://arxiv.org/abs/2503.23820v3
• Date: Thu, 10 Apr 2025 14:30:12 GMT
• Title: When Counterfactual Reasoning Fails: Chaos and Real-World Complexity
• Authors: Yahya Aalaila, Gerrit Großmann, Sumantrak Mukherjee, Jonas Wahl, Sebastian Vollmer,
• Abstract summary: We investigate the limitations of counterfactual reasoning within the framework of Structural Causal Models.<n>We find that realistic assumptions, such as low degrees of model uncertainty or chaotic dynamics, can result in counterintuitive outcomes.<n>This work urges caution when applying counterfactual reasoning in settings characterized by chaos and uncertainty.
• Score: 1.9223856107206057
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Counterfactual reasoning, a cornerstone of human cognition and decision-making, is often seen as the 'holy grail' of causal learning, with applications ranging from interpreting machine learning models to promoting algorithmic fairness. While counterfactual reasoning has been extensively studied in contexts where the underlying causal model is well-defined, real-world causal modeling is often hindered by model and parameter uncertainty, observational noise, and chaotic behavior. The reliability of counterfactual analysis in such settings remains largely unexplored. In this work, we investigate the limitations of counterfactual reasoning within the framework of Structural Causal Models. Specifically, we empirically investigate \emph{counterfactual sequence estimation} and highlight cases where it becomes increasingly unreliable. We find that realistic assumptions, such as low degrees of model uncertainty or chaotic dynamics, can result in counterintuitive outcomes, including dramatic deviations between predicted and true counterfactual trajectories. This work urges caution when applying counterfactual reasoning in settings characterized by chaos and uncertainty. Furthermore, it raises the question of whether certain systems may pose fundamental limitations on the ability to answer counterfactual questions about their behavior.

Related papers

• Causality can systematically address the monsters under the bench(marks) [64.36592889550431]
  Benchmarks are plagued by various biases, artifacts, or leakage.<n>Models may behave unreliably due to poorly explored failure modes.<n> causality offers an ideal framework to systematically address these challenges.
  arXiv  Detail & Related papers  (2025-02-07T17:01:37Z)
• Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning [11.13665894783481]
  Causal opacity denotes the difficulty in understanding the "hidden" causal structure underlying the decisions of deep neural network (DNN) models.<n>This work introduces Causal Concept Graph Models (Causal CGMs), a class of interpretable models whose decision-making process is causally transparent by design.<n>Our experiments show that Causal CGMs can: (i) match the generalisation performance of causally opaque models, (ii) enable human-in-the-loop corrections to mispredicted intermediate reasoning steps, and (iii) support the analysis of interventional and counterfactual scenarios.
  arXiv  Detail & Related papers  (2024-05-26T10:15:20Z)
• Targeted Reduction of Causal Models [55.11778726095353]
  Causal Representation Learning offers a promising avenue to uncover interpretable causal patterns in simulations. We introduce Targeted Causal Reduction (TCR), a method for condensing complex intervenable models into a concise set of causal factors. Its ability to generate interpretable high-level explanations from complex models is demonstrated on toy and mechanical systems.
  arXiv  Detail & Related papers  (2023-11-30T15:46:22Z)
• Interpretable Imitation Learning with Dynamic Causal Relations [65.18456572421702]
  We propose to expose captured knowledge in the form of a directed acyclic causal graph. We also design this causal discovery process to be state-dependent, enabling it to model the dynamics in latent causal graphs. The proposed framework is composed of three parts: a dynamic causal discovery module, a causality encoding module, and a prediction module, and is trained in an end-to-end manner.
  arXiv  Detail & Related papers  (2023-09-30T20:59:42Z)
• On Continuity of Robust and Accurate Classifiers [3.8673630752805437]
  It has been shown that adversarial training can improve the robustness of the hypothesis. It has been suggested that robustness and accuracy of a hypothesis are at odds with each other. In this paper, we put forth the alternative proposal that it is the continuity of a hypothesis that is incompatible with its robustness and accuracy.
  arXiv  Detail & Related papers  (2023-09-29T08:14:25Z)
• Towards CausalGPT: A Multi-Agent Approach for Faithful Knowledge Reasoning via Promoting Causal Consistency in LLMs [55.66353783572259]
  Causal-Consistency Chain-of-Thought harnesses multi-agent collaboration to bolster the faithfulness and causality of foundation models. Our framework demonstrates significant superiority over state-of-the-art methods through extensive and comprehensive evaluations.
  arXiv  Detail & Related papers  (2023-08-23T04:59:21Z)
• Towards Characterizing Domain Counterfactuals For Invertible Latent Causal Models [15.817239008727789]
  In this work, we analyze a specific type of causal query called domain counterfactuals, which hypothesizes what a sample would have looked like if it had been generated in a different domain. We show that recovering the latent Structural Causal Model (SCM) is unnecessary for estimating domain counterfactuals. We also develop a theoretically grounded practical algorithm that simplifies the modeling process to generative model estimation.
  arXiv  Detail & Related papers  (2023-06-20T04:19:06Z)
• Advancing Counterfactual Inference through Nonlinear Quantile Regression [77.28323341329461]
  We propose a framework for efficient and effective counterfactual inference implemented with neural networks. The proposed approach enhances the capacity to generalize estimated counterfactual outcomes to unseen data. Empirical results conducted on multiple datasets offer compelling support for our theoretical assertions.
  arXiv  Detail & Related papers  (2023-06-09T08:30:51Z)
• Mining Causality from Continuous-time Dynamics Models: An Application to Tsunami Forecasting [22.434845478979604]
  We propose a mechanism for mining causal structures from continuous-time models. We train models to capture the causal structure by enforcing sparsity in the weights of the input layers of the dynamics models. We apply our method to a real-world problem, namely tsunami forecasting, where the exact causal-structures are difficult to characterize.
  arXiv  Detail & Related papers  (2022-10-10T18:53:13Z)
• Neural Causal Models for Counterfactual Identification and Estimation [62.30444687707919]
  We study the evaluation of counterfactual statements through neural models. First, we show that neural causal models (NCMs) are expressive enough. Second, we develop an algorithm for simultaneously identifying and estimating counterfactual distributions.
  arXiv  Detail & Related papers  (2022-09-30T18:29:09Z)
• Causal World Models by Unsupervised Deconfounding of Physical Dynamics [20.447000858907646]
  The capability of imagining internally with a mental model of the world is vitally important for human cognition. We propose Causal World Models (CWMs) that allow unsupervised modeling of relationships between the intervened and alternative futures. We show reductions in complexity sample for reinforcement learning tasks and improvements in counterfactual physical reasoning.
  arXiv  Detail & Related papers  (2020-12-28T13:44:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.