Tigramite – Causal inference for time series datasets - GitHub
Tigramite is a causal inference for time series python package. It allows to efficiently estimate causal graphs from high-dimensional time series datasets (causal discovery) and to use graphs for robust forecasting and the estimation and prediction of direct, total, and mediated effects.
Inferring causation from time series in Earth system sciences
Jun 14, 2019 · PCMCI systematically identifies common drivers and indirect links among time-lagged variables, in this particular example based on partial correlation tests. Details on data in ref. 53.
Welcome to Tigramite’s documentation! — Tigramite 5.2 …
PCMCI is a causal discovery framework for large-scale time series datasets. This class contains several methods. The standard PCMCI method addresses time-lagged causal discovery and is described in Ref [1] where also further sub-variants are discussed.
Detecting and quantifying causal associations in large nonlinear …
Nov 27, 2019 · PCMCI addresses the challenges of large-scale multivariate, autocorrelated, linear, and nonlinear time series datasets opening up new opportunities to more credibly discover causal networks and estimate causal effects in many areas of science.
Causal inference for time series - Nature Reviews Earth
Jun 27, 2023 · In this Technical Review, we explain the use of causal inference frameworks with a focus on the challenges of time series data. Domain-adapted explanations, method guidance and practical case...
Causal Discovery with Multivariate Time Series Data
Jan 8, 2024 · PCMCI is an algorithm for causal discovery in a multivariate time-series setting that addresses the problems of high dimensionality by conditioning on a conditioning set that at least includes...
Methods and tools for causal discovery and causal inference
Jan 19, 2022 · The PCMCI (Runge et al., 2019) is a causal graphical method designed to deal with linear and nonlinear time series. This algorithm is divided into two phases, each one corresponding to a different conditional independence test: the PC1 and MCI phases.
GitHub - dcacciarelli/pcmci: Tigramite is a python package for …
Tigramite is a causal inference for time series python package. It allows to efficiently estimate causal graphs from high-dimensional time series datasets (causal discovery) and to use graphs for robust forecasting and the estimation and prediction of direct, total, and mediated effects.
Causal networks for climate model evaluation and constrained …
Mar 16, 2020 · PCMCI avoids conditioning on all variables by an efficient condition-selection step (PC) that iteratively performs conditional independence tests to identify the typically few relevant necessary...
GitHub - guenwi/J-PCMCIplus: Code for reproducing the …
It then computes the output of the J-PCMCI+, PCMCI+ with observed context variables, PCMCI+ with dummy variables and original PCMCI+ on system variables. Using the ground truth graphs and output graphs of these methods, it calculates the metrics which are described in more detail in the paper, and plots the results. The results are as follows: