microsoft / dowhy

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

Summary

	IN CFG
		Main Code: 6,378 LOC (71 files) = PY (98%) + YML (<1%) + IN (<1%) + CFG (<1%) Secondary code: Test: 2,126 LOC (36); Generated: 0 LOC (0); Build & Deploy: 0 LOC (0); Other: 402 LOC (2);
		Duplication: 10%
		File Size: 0% long (>1000 LOC), 52% short (<= 200 LOC)
		Unit Size: 10% long (>100 LOC), 37% short (<= 10 LOC)
		Conditional Complexity: 2% complex (McCabe index > 50), 43% simple (McCabe index <= 5)
		Logical Component Decomposition: primary (12 components)
		3 years, 8 months old 85% of code older than 365 days 17% of code not updated in the past 365 days
		0% of code updated more than 50 times Also see temporal dependencies for files frequently changed in same commits.
		Goals: Keep the system simple and easy to change (4)
	Features of interest: TODOs 16 files

Commits Trend

Latest commit date: 2022-01-27

10 commits (30 days) 5 contributors (30 days)

Commits 10 53 163 180 72 Contributors 5 12 10 14 17 2022 2021 2020 2019 2018

show commits trend per language

Commits	2022 2021 2020 2019 2018 9 36 104 123 23 py 5 10 9 yml in 1 1 1 5 in cfg 1 cfg
Contributors	2022 2021 2020 2019 2018 4 9 5 11 7 py 2 1 2 yml in 1 1 1 2 in cfg 1 cfg