opttx - Optimal Treatment Estimation

Causal inference with multiple treatments

Approximately balanced estimation of average treatment effects in high dimensions.

This project has scripts which can be used to compress test vectors or patterns generated to use on SoC testers like Advantest, Ultraflex. The concept is universal and can be extended to any SoC tester.

Implements SemRe-Rank: improving automatic term extraction by incorporating semantic relatedness with personalised pagerank

STDF to Wafer Bin Map utility written in Python

R package for fast and easy doubly robust estimation of treatments effects

Caret Implementation of GRF

Integrative analyses of the average treatment effect combining big main data and smaller validation data

Quasi-Oracle Estimation of Heterogeneous Treatment Effects

Simulation of Benkeser D, Cai W, van der Laan MJ (2019+). A nonparametric super-efficient estimator of the average treatment effect.

Generalized Random Forests

API to calculate odometry evaluation metrics.

A Node-RED nodes to work with EEZ BB3

Node-RED flow for automated testing of DCP405 module

A project in the course of Causal Inference

Automated Test Equipment lab source code for the Master of Electronics and ICT course of Hardware Design at KU Leuven 2020-2021

Non-parametric variable selection and inference via the outcome-adaptive Random Forest (OARF). Uses the IPTW estimator to estimate the ATE while the propensity score is estimated via OARF. This leads to smaller variance and bias. Only variables that are confounders or predictive of the outcome are selected for the propensity score.