---
pattern_id: pattern_deepmind_causal_reasoning
applicable_symptoms: [deepmind_causal_reasoning]
domain: Learning_Training
---

# Standard deep learning models learn correlations P(Y|X) and fail under distribution shift, preventing robust generalization and safe intervention planning in embodied AI.

**Domain**: `Learning_Training`

## Fix

Use causal representation learning (e.g., Causal VAEs, independent mechanism analysis) and causal model-based RL to learn structural causal models that support interventions and counterfactuals.

## Anti-pattern

Purely associative deep learning that ignores causal structure.

## Cross-domain analogies

- **Perception_Vision** → Use variance-based filtering to discard high-uncertainty training samples under distribution shift.
  - related fix: Laplacian Variance Filtering (LVF): compute variance of Laplacian of each frame; discard or deweight frames with low variance (high blur) to reduce jitter-induced motion blur.
- **Planning_Decision** → Use a dual-relation reasoning pipeline with synthetic chain-of-thought data to model causal mechanisms P(Y|do(X)) over spurious correlations.
  - related fix: Use a dual-relation reasoning framework with a Chain-of-Thought (CoT) pipeline that first models target-environment relations (e.g., object co-occurrence, spatial priors) and then environment-action relations (e.g., feasible exploration steps), fine-tuned on synthetic CoT data generated by Qwen2.5-VL-7B.
- **Control_Locomotion** → Pre-train a library of invariant causal representations via self-supervised learning, decoupling robust features from task-specific correlations.
  - related fix: Pre-train a library of versatile locomotion and interaction behaviors via reinforcement learning, decoupling skill acquisition from task planning.

## Patch

```diff
--- deepmind_causal_reasoning.before.py
+++ deepmind_causal_reasoning.after.py
@@ -1,2 +1,4 @@
-# --- BEFORE (vulnerable to the symptom below) ---

+# --- AFTER (ROSCLAW heuristic graft) ---

 # Symptom: Standard deep learning models learn correlations P(Y|X) and fail under distribution shift, preventing robust generalization and safe intervention planning in embodied AI.

+# Fix    : Use causal representation learning (e.g., Causal VAEs, independent mechanism analysis) and causal model-based RL to learn structural causal models that support interventions and counterfactuals.

+# Avoid  : Purely associative deep learning that ignores causal structure.

```
