Experimental causal-inference research on financial regimes: PCMCI+, ICP and causal forests over market data (work in progress)
-
Updated
Aug 11, 2026 - Python
Experimental causal-inference research on financial regimes: PCMCI+, ICP and causal forests over market data (work in progress)
General-purpose Python toolkit for horizon-wise forecastability analysis using interchangeable dependence scorers, with AMI/pAMI support, rolling-origin benchmarking, and reproducible reporting.
Causal discovery pipeline for Bitcoin return drivers — PC Algorithm, NOTEARS, PCMCI, Granger + DoWhy falsification. In partnership with ESILV and Ginjer AM.
Add a description, image, and links to the tigramite topic page so that developers can more easily learn about it.
To associate your repository with the tigramite topic, visit your repo's landing page and select "manage topics."