Causal Confusion in Inference & Learning

• Causal confusion is a misattribution issue where algorithms detect spurious correlations instead of true causal factors, leading to flawed outcomes.
• It arises from incomplete or mis-specified adjustment for confounders, impacting fields like reinforcement, imitation, and preference-based learning.
• Mitigation strategies such as human-guided regularization, active data sampling, and structural interventions help models focus on valid causal relationships.

Causal confusion refers to the phenomenon in which decision-makers or learning algorithms systematically misattribute the causal effects between variables, often by exploiting or relying on spurious correlations present in observational data and failing to model the true underlying causal structure. This misattribution arises across diverse domains—causal inference, imitation learning, reinforcement learning, and automated question-answering—and is exacerbated whenever adjustment for confounders is incomplete, mis-specified, or based on features that are merely correlated but not causally involved.

1. Formal Definitions and Theoretical Frameworks

Causal confusion occurs when a model or policy attributes outcomes to factors that are not the true causes of those outcomes but are merely correlated with them due to confounding, covariability, selection bias, or other structural artifacts. Formally, in potential-outcome or structural equation frameworks, the observed association $P(y|x)$ is conflated with the causal effect $E[y|do(x)]$, and naive adjustment fails to resolve this unless all confounders are controlled appropriately and no other spurious associations persist (Spiegler, 2023, Ledberg, 2018):

• In imitation learning, causal confusion manifests when a policy $\pi(a|o)$ exploits features in observation $o$ that are not true determinants of the expert action $a$, but happen to correlate with $a$ due to dataset-specific characteristics (Haan et al., 2019, Park et al., 2021, Sanchez et al., 2024, Banayeeanzade et al., 25 Jul 2025).
• In reinforcement learning, causal confusion describes learned value functions or policies that depend on features spuriously associated with reward in the training data; deployment in the real environment where those correlations fail can result in catastrophic drop in performance (Gupta et al., 2023, Gajcin et al., 2022).
• In answer generation and preference-based learning settings, systems may present or optimize arguments based on correlational evidence, misleading users into accepting causally invalid claims (Law et al., 2020, Tien et al., 2022).

The formal criteria for causal confusion often involve interventions: for any variables $s$ (causal factors) and $t$ (confounders/distractors), a robust model should satisfy invariance $$P(\pi(o) | do(s), do(t)) = P(\pi(o) | do(s), do(t'))$$ for all $t, t'$, whereas a confused model would violate this (Banayeeanzade et al., 25 Jul 2025).

2. Origins: Confounding, Covariability, and Mis-Specification

Confounding arises when an unmeasured variable $U$ influences both the treatment $X$ and the outcome $Y$, so their observed correlation reflects both direct and indirect effects. Standard adjustment techniques—stratifying on confounders or including them as regression covariates—fail when the effects of confounders co-vary across units, i.e., the causal effects themselves are heterogeneous and not independent. In such cases, even perfect stratification on $Z$ leaves a residual $Cov(X, Y | Z) \neq 0$ if $Cov_U(\alpha_X(U), \alpha_Y(U)) \neq 0$ for some effect functions $\alpha_X, \alpha_Y$ (Ledberg, 2018). This latent “slope covariance” cannot be removed except by conditioning on the unobserved $U$, deploying instruments, or using repeated measures (Marzban et al., 23 Jun 2025).

In aggregated causal systems, causal confusion arises when macro-level interventions (e.g., setting $\bar{X}$) do not specify the realization at the micro level, so the resulting causal effect of $\bar{X} \to \bar{Y}$ can be confounded or unconfounded depending on the chosen micro-intervention. Only “natural” macro interventions—sampling the microstate from its observational conditional law—guarantee absence of macro-level confounding when the micro model is unconfounded (Zhu et al., 2023).

Control variable mis-specification in observational causal inference can generate confusion: including associative covariates (variables associated with both treatment $T$ and outcome $Y$ but not actually causally involved) can introduce extraneous bias (e.g., M-bias), while excluding them may leave residual confounding. The best action—include or exclude—depends on the unknown dependence structure of hidden causes and the empirical strength of associations (Wijayatunga, 2018).

3. Empirical Manifestations and Diagnosis

Causal confusion in learning algorithms can be identified through several empirical symptoms:

• Distributional shift sensitivity: Model exhibits high fit (low training or test error) on observational data but performs poorly under intervention or policy-induced test-time data, indicating reliance on spurious correlations likely broken at deployment (Haan et al., 2019, Tien et al., 2022, Huang et al., 11 Nov 2025).
• Performance gap: Large discrepancies between open-loop (training or offline evaluation) and closed-loop (real or online deployment) metrics signal causal confusion, especially in offline RL and autonomous driving benchmarks (Gupta et al., 2023, Huang et al., 11 Nov 2025).
• “More information → worse performance” phenomenon: Including additional (non-causal) features can degrade generalization even as supervised losses improve, a hallmark symptom in behavioral cloning and reward learning (Haan et al., 2019, Tien et al., 2022, Park et al., 2021).
• Confusion amplification through equilibrium effects: In multi-agent or heterogeneous decision-maker models, “horizontal differentiation” of control sets can generate reinforcing spurious correlations, persisting even in equilibrium (Spiegler, 2023).
• Preference learning misidentification: In PBRL, even a learned reward function achieving minimal test error may encode deeply spurious associations, yielding an RL-optimized policy with low true reward (Tien et al., 2022).

Table: Causal Confusion Symptoms Across Domains

Domain	Key Syntomatic Feature	Typical Diagnostic
Imitation Learning	Out-of-distribution collapse after training	Test–train gap, policy–reward mismatch
Reinforcement Learning	Open-loop / closed-loop mismatch	RL deployment score drop
Causal Inference	Model fit diverges from true intervention effect	Post-adjustment residual association
Aggregated Systems	Ambiguity in macro-level causal effect	Micro–macro inconsistency

4. Mitigation and Algorithms

Several algorithmic paradigms and interventions have demonstrated efficacy in resolving or reducing causal confusion:

Active sampling and uncertainty-based data selection: In offline RL, variance-based, TD-error-based, or ensemble acquisition functions preferentially sample transitions where the estimated advantage or Q-value is most uncertain, exposing the agent to tail cases that break spurious correlations and thus repairing causal credit assignment more rapidly than uniform sampling (Gupta et al., 2023).

Human-guided regularization and supervision: Human-placed beacons (Sanchez et al., 2024) and expert gaze data (Banayeeanzade et al., 25 Jul 2025) anchor representation learning to causally relevant features during imitation demonstration, reducing reliance on distractors and enhancing explainability. RECON trains the feature encoder to maximize mutual information with beacon measurements, and GABRIL regularizes the final convolutional features toward expert gaze saliency maps.

Object-aware and structural regularization: OREO enforces uniform attention over semantic objects discovered by a VQ-VAE, randomly dropping all pixels of a given object code and thus preventing fixation on single nuisance features (Park et al., 2021). Graph-parameterized mixture policies and targeted interventions (as in CCIL and Causal-ACT) search the space of candidate structural functions through rollouts and expert queries, selecting the parent subset yielding highest reward and thereby pruning non-causal features (Haan et al., 2019, Chen et al., 30 Jul 2025).

Perception-guided self-supervision: PGS replaces noisy expert behaviors with structured perception signals and positive/negative self-supervision objectives aligned with the underlying road geometry and agent forecasting. This avoids policy overfitting to latent trajectory noise in autonomous driving (Huang et al., 11 Nov 2025).

Feature-based intervention and selection: Algorithms like ReCCoVER train sub-policies on all subsets of features, intervene in critical states to break suspect associations, and recommend alternative feature sets for retraining in regions affected by causal confusion (Gajcin et al., 2022).

5. Practical Guidelines and Limitations

A recurring recommendation is to explicitly model and test the causal relationships between inputs and decisions, using interventions, invariance checks, and diagnostic measures such as gradient saliency maps, reward preference discrepancies, and distribution-shift metrics (EPIC, KL divergence) (Tien et al., 2022).

Researchers should be wary of blanket inclusion or exclusion of associative covariates in regression adjustment, and instead utilize domain knowledge, DAG analysis, or sensitivity check to assess the risk and magnitude of any residual bias (Wijayatunga, 2018). In observational studies, only nested or “vertically ordered” control sets—where every richer set contains the variables conditioned on by poorer sets—guarantee equilibrium safety against causal confusion (Spiegler, 2023).

Recognition of causal confusion often demands access to interventions, real or simulated; purely offline regularization (as in OREO or GABRIL) can mitigate confusion, but the gold standard remains active querying, rollouts, or environment modification (Haan et al., 2019, Gupta et al., 2023). Limitations persist in complex, high-dimensional settings, where scalable generation of interventions, automatic feature subset selection, and reliance on human input remain challenging.

6. Open Questions and Future Directions

Promising open directions include the development of improved sensitivity analysis tools for causal-effect covariability, automated active beacon placement, automated region clustering for intervention generation, and systematic integration of perception-driven supervision, human attention, and structure learning architectures in end-to-end RL and self-driving pipelines (Ledberg, 2018, Sanchez et al., 2024, Huang et al., 11 Nov 2025).

Generalization of these paradigms to continuous action spaces, large-scale vision tasks, batch-to-online RL, and sim-to-real transfer, and their integration with pessimism and uncertainty-aware RL remains a subject of ongoing work (Gupta et al., 2023). Automation of region extraction and subset assignment in causal confusion detection—particularly for high-dimensional observations—remains an unsolved problem (Gajcin et al., 2022).

Careful attention to micro–macro correspondence and the specification of aggregated interventions is essential for principled causal effect estimation in non-elementary systems (Zhu et al., 2023).

7. Summary

Causal confusion is a persistent and general obstacle to valid causal inference and robust learning from observational or preference data. It is fundamentally rooted in inadequately specified adjustment sets, confounder covariability, or model architectures that fail to encode causal invariances. Only through a combination of principled intervention, analysis, active data selection, and human-in-the-loop supervision can researchers hope to reliably resolve causal confusion and achieve generalizable, causally valid models (Spiegler, 2023, Gupta et al., 2023, Ledberg, 2018, Sanchez et al., 2024, Huang et al., 11 Nov 2025, Zhu et al., 2023, Haan et al., 2019, Law et al., 2020, Marzban et al., 23 Jun 2025, Wijayatunga, 2018, Banayeeanzade et al., 25 Jul 2025, Chen et al., 30 Jul 2025, Park et al., 2021, Tien et al., 2022, Gajcin et al., 2022).