Econometrics MCP server with regression, causal inference, time series, panel data, and statistical analysis workflows.
Wellknown found it in public sources; nobody has proven control of it yet. Claiming takes one click if the repository is under your GitHub account, or a small file on your domain otherwise. Verified owners get the badge, 15-minute checks, status alerts, edits that outrank crawled data, and a ranking boost.
Agents can do it too: POST https://wellknown.network/api/v1/claims with {"agent":"aigroup-econ-mcp","method":"well_known_file"} — machine-readable steps at claim.json, guide at /docs/claim.
Everything here was measured by our prober or read from a registry. Nothing is self-reported.
Attributed to the source that supplied each field. Treated as claims, not facts.
# aigroup-econ-mcp [](./LICENSE) [](https://www.python.org/) [](https://github.com/jackdark425/aigroup-econ-mcp) [](https://github.com/jackdark425/aigroup-econ-mcp) > Econometrics MCP server for regression, causal inference, time series, panel data, machine learning, and broader statistical analysis workflows. ## Overview `aigroup-econ-mcp` is a professional econometrics-oriented MCP server designed to help AI assistants and MCP clients perform structured quantitative analysis. It covers: - parameter estimation and regression analysis - causal inference workflows - microeconometrics and panel data - time series and volatility models - machine learning for econometric tasks - spatial econometrics, decomposition, and inference tools ## Highlights - **66 professional tools** across core econometrics domains - **Multiple input formats** including CSV, JSON, TXT, and Excel - **Multiple output formats** including JSON, Markdown, and text - **Support for MCP clients** such as RooCode, Claude-compatible tools, and other MCP hosts - **Broad method coverage** from OLS and IV to ARIMA, GARCH, GAM, and causal forests - **Designed for research and applied analysis** rather than narrow single-task workflows ## Tool Groups The server currently groups its 66 tools across the following categories: - **Basic parametric estimation** — OLS, MLE, GMM - **Causal inference** — DID, IV, PSM, fixed/random effects, RDD, synthetic control, event study, and more - **Decomposition analysis** — Oaxaca-Blinder, ANOVA, time-series decomposition - **Machine learning** — random forest, gradient boosting, SVM, neural networks, clustering, DML, causal forest - **Microeconometrics** — logit, probit, multinomial logit, Poisson, negat…
Mapped onto the structured taxonomy from declared text and observed tool names. Confidence shown for derived entries.
Every source is kept verbatim. Field changes are logged as events.