# Black-Swan-Causal-Labs/dag-studio-mcp

Source: https://glimind.com/server/glama%2Fblack-swan-causal-labs%2Fdag-studio-mcp
Registry id: `glama/black-swan-causal-labs/dag-studio-mcp`
Status: unknown

## Tools (9)

- `analyze_dag`: Backdoor paths, minimal sufficient adjustment sets, identifiability
- `check_overadjustment`: Detect adjustment for mediators, colliders, and descendants of exposure
- `classify_effect_modification`: Classify effect-modifier structure (direct, indirect, proxy, common-cause, pure interaction)
- `compute_bias`: Empirical bias of an adjustment strategy against the simulated truth
- `generate_code`: R / Python analysis code for a DAG, plus a one-click DAG Studio URL
- `get_canonical_example`: Canonical teaching DAGs (confounding, M-bias, frontdoor, and others)
- `parse_dagitty`: Parse dagitty DSL (raw or R-wrapped) into the structured DAG model
- `simulate_data`: Simulate data from a DAG under user-specified structural coefficients
- `validate_engine`: Run the full canonical validation suite and report engine version

Measured by Glimind (https://glimind.com): MCP handshake + tool list only, no tool is ever called. CC BY 4.0.