Phase 40 Planning — Causal Reasoning & Interventional Intelligence

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Phase 40 — Causal Reasoning & Interventional Intelligence

Vision

Phase 40 brings causal reasoning to ASI-Build, moving beyond correlation-based inference to true interventional and counterfactual intelligence. Building on Phase 39's Explainable AI foundation, this phase implements Pearl's causal hierarchy (association → intervention → counterfactual) as a unified computational framework.

Sub-Phase Roadmap

Sub-Phase	Component	Focus
40.1	CausalGraphBuilder	DAG construction, causal discovery (PC, FCI, GES), structural causal models
40.2	InterventionEngine	do-calculus, truncated factorization, backdoor/frontdoor criteria, ATE estimation
40.3	CounterfactualReasoner	Structural counterfactuals, twin networks, potential outcomes, counterfactual fairness
40.4	CausalInferenceOptimizer	Doubly robust estimation, IPW, matching, synthetic controls, DiD, RDD
40.5	CausalOrchestrator	Unified causal pipeline, model registry, intervention planning, XAI integration

Architecture Overview

┌─────────────────────────────────────────────────────────────┐
│                    CausalOrchestrator (40.5)                 │
│  ┌──────────────────────────────────────────────────────┐   │
│  │              Causal Model Registry                    │   │
│  └──────────────────────────────────────────────────────┘   │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────────┐  │
│  │ CausalGraph  │  │ Intervention │  │ Counterfactual   │  │
│  │ Builder 40.1 │→ │ Engine  40.2 │→ │ Reasoner   40.3  │  │
│  └──────────────┘  └──────────────┘  └──────────────────┘  │
│  ┌──────────────────────────────────────────────────────┐   │
│  │          CausalInferenceOptimizer (40.4)              │   │
│  │  IPW | Matching | Doubly Robust | Synthetic Control   │   │
│  └──────────────────────────────────────────────────────┘   │
│  ┌──────────────────────────────────────────────────────┐   │
│  │      Phase 39 XAI — ExplainabilityOrchestrator        │   │
│  └──────────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────────┘

Academic References

1. Pearl (2009) — Causality: Models, Reasoning, and Inference (2nd ed.)
2. Pearl (2018) — The Book of Why
3. Peters, Janzing & Schölkopf (2017) — Elements of Causal Inference
4. Spirtes, Glymour & Scheines (2000) — Causation, Prediction, and Search
5. Rubin (1974) — Estimating Causal Effects of Treatments
6. Imbens & Rubin (2015) — Causal Inference for Statistics, Social, and Biomedical Sciences
7. Schölkopf et al. (2021) — Toward Causal Representation Learning
8. Bareinboim & Pearl (2016) — Causal inference and the data-fusion problem
9. Glymour, Zhang & Spirtes (2019) — Review of Causal Discovery Methods
10. Chernozhukov et al. (2018) — Double/debiased machine learning
11. Kusner et al. (2017) — Counterfactual Fairness

Relationship to Phase 39 (Explainable AI)

Phase 39 answers "what does the model see?" while Phase 40 answers "what would happen if we changed X?" The ExplainabilityOrchestrator (39.5) provides feature attributions and concept explanations that feed into CausalGraphBuilder (40.1) as candidate causal variables. Counterfactual explanations from Phase 39's CounterfactualGenerator (39.3) are elevated to full structural counterfactuals in Phase 40.3, grounded in proper SCMs rather than nearest-neighbor perturbations.