The Model Context Protocol (MCP) server provides Causal DAG analysis capabilities, focusing on identifying backdoor paths, deriving adjustment sets, and running bias simulations. Its functionality is validated against dagitty, indicating alignment with that tool’s causal DAG analysis behavior.

🛠️ Key Features

Backdoor path identification
Adjustment set generation
Bias simulation
Validation against dagitty

🚀 Use Cases

Analyze causal graphs using DAGs
Determine which variables to adjust for causal inference
Simulate potential bias effects in causal settings

dag-studio-mcp

Model Context Protocol (MCP) server for the DAG Studio causal-inference engine. It lets AI agents construct, analyze, and validate causal directed acyclic graphs (DAGs) using the same engine that powers the DAG Studio canvas.

Built and maintained by Black Swan Causal Labs. Listed in the RWE MCP Registry.

Tools

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

Every analytical response carries an engine_version stamp, a concordance attestation, a diagnostics block with severity-coded flags, and citations to the underlying methods literature.

Validation

The engine is validated four ways for coherence: against Pearl (2009) theory, against the reference implementation dagitty (Textor et al. 2016), against DAG Studio's own analytical results, and empirically via compute_bias on simulated data.

35 canonical cases: T01 to T15 (structural identification) and EM01 to EM20 (effect modification), runnable live via validate_engine.
A release-gate concordance check runs the engine head-to-head against dagitty (vendored at upstream commit 7a65777) and stamps the attestation surfaced in tool responses.
93 unit and integration tests across the engine bindings, the tool layer, and the auth gate.

Hosted endpoint

The server runs as a Cloudflare Worker (Streamable HTTP):

code

https://dagstudio-mcp.blackswancausallabs.com/mcp

Access is token-gated during the trial period. Request a token at jdiazdecaro@blackswancausallabs.com. Tokens are accepted either as a bearer header or as a ?token= query parameter (the query form exists for clients whose connector UI cannot set custom headers, such as the Claude.ai web connector).

Claude Code:

claude mcp add --transport http dag-studio \
  https://dagstudio-mcp.blackswancausallabs.com/mcp \
  --header "Authorization: Bearer <your token>"

Claude.ai web: add a custom connector pointed at https://dagstudio-mcp.blackswancausallabs.com/mcp?token=<your token>.

Repository layout

dag-engine.js / dag-engine.d.ts: the analytical engine, a pure ESM module with no runtime dependencies
src/tools/: one file per tool, each exporting { InputSchema, OutputSchema, descriptor, handler }
src/worker/: Cloudflare Worker transport and the token gate (auth.ts)
src/index.ts: stdio entry point for local use
ci/: release-gate concordance harness against vendored dagitty
tests/: unit and integration tests (npm test)
MCP_REQUIREMENTS.md: the v1 specification
FDA_GUIDANCE_ALIGNMENT.md: mapping of DAG Studio capabilities onto FDA draft RWE guidance protocol elements

The engine is developed alongside the DAG Studio canvas app and the vendored copy here is synced at release time. This repository is self-contained: clone it, npm install, and everything builds and tests without further setup.

Development

npm install
npm test                  # full suite
npm run dev               # stdio server via tsx
npm run worker:typecheck  # worker bundle typecheck
npm run worker:deploy     # deploy (stamps engine_version from git HEAD first)

For interactive inspection: npx @modelcontextprotocol/inspector.

Protocol status

Built on @modelcontextprotocol/sdk (TypeScript). Current against the finalized MCP specification revision 2025-11-25. Migration to the 2026-07-28 revision is planned once stable SDK support ships.

License

Apache License 2.0 (see LICENSE).

Exception: ci/dagitty-src/ contains the dagitty reference engine (GPL-2.0, Textor et al.), vendored at upstream commit 7a65777 solely as a release-time CI fixture for the concordance check. It retains its own license, is excluded from the published npm package, and is not part of the deployed worker bundle.

DAG Studio MCP