# jakobrunge/tigramite

Tigramite is a python package for causal inference with a focus on time series data. The Tigramite documentation is at

Repository: https://github.com/jakobrunge/tigramite
Canonical: https://ross.abutalabs.com/products/tigramite
Homepage: https://jakobrunge.github.io/tigramite/
Language: Jupyter Notebook
License: GPL-3.0
License Family: copyleft
Last push: 2026-01-14T22:41:36+00:00

## Health v2 (maintenance only)
Score: 51/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 62, release rhythm 8, longevity 100
- inputs: {"age_days": 3397, "days_push": 231, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1710, forks 319 (observed 2026-08-28T04:05:25.615224+00:00)

## What it is
Tigramite is a Python package for causal inference on time series data, providing causal discovery methods such as PCMCI, PCMCIplus, LPCMCI, and RPCMCI together with a range of conditional independence tests. It also supports causal effect estimation, mediation analysis, robust forecasting from learned graphs, and plotting of results.

## Use cases
- estimate causal graphs from time series data
- discover lagged and contemporaneous causal links in autocorrelated datasets
- estimate direct, total, and mediated causal effects from a known causal graph
- run conditional independence tests on continuous or discrete time series
- analyze regime-dependent causal relationships
- perform causal time series analysis in climate or geoscience research

## When to choose
- you need causal discovery specifically designed for time series with lags
- you want a choice of linear and non-parametric conditional independence tests
- you need to handle hidden confounders or multiple datasets in causal discovery
- you want to estimate causal effects or mediation from time series graphs

## When to avoid
- you need causal discovery for purely cross-sectional (non-time-series) data
- you want a general-purpose machine learning forecasting library without causal structure
- you need a GUI-based causal analysis tool rather than a Python API

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, data-science, math
- domain: data-science, machine-learning
- platform: python, cross-platform
- tags: causal-inference, causal-discovery, time-series, conditional-independence-tests, pcmci, causal-effects, algorithms

## Member repositories
- jakobrunge/tigramite (main) score 51

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:25.615224+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:35:08.823722+00:00, confidence not recorded.
  - readme: https://github.com/jakobrunge/tigramite (fetched 2026-08-28T04:05:25.615224+00:00, sha 80023ba91e01)
  - homepage: https://jakobrunge.github.io/tigramite/ (fetched 2026-08-29T11:11:08.410615+00:00, sha f80a5d2c9d33)
  - registry_pypi: https://pypi.org/pypi/tigramite/json (fetched 2026-08-29T11:11:08.424909+00:00, sha 25c715349512)
- Data as of 2026-08-30T08:39:29.467469+00:00.