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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.

[CVPR 2023 Best Paper Award] Planning-oriented Autonomous Driving

  • autonomous-driving
  • end-to-end-autonomous-driving
  • motion-prediction
  • multi-object-tracking

This project hosts security advisories and their accompanying proof-of-concepts related to research conducted at Google which impact non-Google owned code.

Anti-laziness skill for AI agents. Core: the Depth Tree method, which splits a task N layers deep and gives every leaf the full time budget of the whole task, so effort multiplies with depth. Grounded in 2025-2026 research on model laziness, underthinking and premature completion.

Verbatim mirror of the git.drupal.org repository for Drupal core. Please see the https://github.com/drupal/drupal#contributing. PRs are not accepted on GitHub.

Tamper Dev is an extension that allows you to intercept and edit HTTP/HTTPS requests and responses as they happen without the need of a proxy. Works across all operating systems (including Chrome OS).