DAG Studio · Black Swan Causal Labs

What's Inside

DAG Studio is two related products.

A drag-and-drop editor and a Model Context Protocol server, sharing one identification engine. Each can be used on its own.

Surface 01

The Editor

A drag-and-drop DAG editor with an in-browser causal-inference engine. Backdoor paths, adjustment sets, identifiability checks. No install required.

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Surface 02

The MCP Server

The same engine exposed as a Model Context Protocol server. AI assistants can query identifiability, suggest adjustment sets, and flag overadjustment, with citations.

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The Editor

Drag-and-drop DAGs, with a real identification engine underneath.

Drag-and-drop DAG construction Build causal diagrams in the browser with a tactile graph editor.
In-browser causal-inference engine Backdoor path enumeration, minimal sufficient adjustment-set search, and identifiability checks, computed locally.
AI DAG Assistant Describe a research question in plain language and get a first-draft causal diagram on the canvas, exposure and outcome on a shared axis, confounders arranged around them. A drafting aid you refine and the engine checks, not a replacement for judgment.
Validation tab with three-way concordance Live comparison against Pearl (2009) and the dagitty R package on a battery of canonical DAGs.
Linear Gaussian SEM data simulator Sanity-check inferences before committing to a study design. Assumptions documented in-app.
Light and dark themes, full mobile responsiveness Designed for cross-functional teams who reason about DAGs from a laptop or a phone.

Adjusted backdoor path highlighted on a canonical DAG depicting confounding.

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The MCP Server

An identification engine an AI assistant can actually call.

Alongside the editor, DAG Studio also runs as a Model Context Protocol (MCP) server. Where the AI DAG Assistant drafts a starting diagram for you to refine, the MCP server does something complementary: it lets an AI assistant query a real causal-inference engine while you reason about study design, checking identifiability, suggesting adjustment sets, and flagging overadjustment, rather than hallucinating about d-separation. The AI proposes; the engine verifies.

Tool surface (v1)

01

Read a DAG Loads a causal diagram written in dagitty's text format so the other tools can work with it. The "import my diagram" step.

02

Find what to adjust for Given an exposure and an outcome, finds the hidden "backdoor" paths that bias the estimate and returns the smallest set of variables to control for to get an unbiased answer.

03

Catch harmful adjustments Takes a proposed adjustment set and flags variables that would add bias rather than remove it: colliders, mediators, and their descendants.

04

Export to R or Python Generates ready-to-run code for the DAG in R (dagitty) or Python (networkx), plus a one-click link to reopen it in the editor.

05

Classify effect modification When a treatment works differently across subgroups, identifies which kind of effect modification is at play, following the VanderWeele-Robins and Weinberg typology.

06

Simulate data from the DAG Generates synthetic data consistent with the DAG (a linear Gaussian model), reproducible from a seed. Useful for testing an analysis, teaching, or demonstrating a bias.

07

Quantify the bias Puts a number on it: computes the true causal effect from the model and shows how far a given adjustment choice lands from it, and in which direction.

08

Look up a reference example Returns a known-correct textbook DAG (classic confounding, M-bias, mediation, and others) by name from the engine's validated library.

09

Verify the engine A self-test an agent can call to confirm the engine still reproduces its validated cases (against Pearl 2009 and dagitty) before relying on its output. The trust signal.

Status

Live as a hosted MCP server on Cloudflare Workers.
Available for trial access on request.

Get access

Request a trial token, then point any client or API that supports remote MCP at https://dagstudio-mcp.blackswancausallabs.com/mcp. That includes Claude.ai connectors, Claude Code, ChatGPT (Business or Enterprise, developer mode), and the OpenAI and Anthropic APIs as a remote MCP tool. Request access →

Release History

Versions & provenance.

Version	Date	Highlights
v2.0Current	Jul 2026	AI DAG Assistant that drafts a causal diagram from a plain-language research question, radial layout for generated DAGs, new About and FAQ sections, and console and canvas usability improvements.
v1.0	2026	Initial release: drag-and-drop editor, in-browser identification engine, bilingual Python and R console, linear Gaussian SEM data simulation, and the education library.

License

MIT

Citation

Preprint forthcoming. Citation block will be updated on release.

Get Involved

Source-available, by design.

DAG Studio's source is provided to pilot partners and reviewers on request, under the MIT license. Inspectable tooling is the right shape for software that may inform regulatory submissions: the engine should not be a black box to the teams and reviewers who rely on it, even though the repository itself is private.

If your team is considering integrating DAG Studio into protocol development workflows, a small pilot program is open. Pilot scope: two protocols, four weeks, written feedback in exchange for early access and direct engine support. Pharmacoepidemiology and real-world evidence teams are the primary audience.

Get in touch →