ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
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| Main Code: 19,414 LOC (117 files) = PY (84%) + PYX (11%) + PXD (1%) + YML (1%) + TEX (<1%) + CFG (<1%) + STY (<1%) + R (<1%) + TOML (<1%) + BIB (<1%) Secondary code: Test: 9,769 LOC (35); Generated: 0 LOC (0); Build & Deploy: 34 LOC (2); Other: 3,147 LOC (24); |
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| Duplication: 20% | |||
| File Size: 5% long (>1000 LOC), 27% short (<= 200 LOC) | |||
| Unit Size: 6% long (>100 LOC), 50% short (<= 10 LOC) | |||
| Conditional Complexity: 5% complex (McCabe index > 50), 63% simple (McCabe index <= 5) | |||
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Logical Component Decomposition: primary (24 components) | ||
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3 years, 9 months old
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0% of code updated more than 50 times Also see temporal dependencies for files frequently changed in same commits. |
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| Goals: Keep the system simple and easy to change (4) | |||
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Features of interest:
TODOs
17 files |
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Latest commit date: 2021-08-13
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