A project in the course of Causal Inference
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Updated
Mar 22, 2021 - Jupyter Notebook
A project in the course of Causal Inference
Simple method for filling in a spreadsheet with defined equations. Useful for electronics testing.
Automated Test Equipment lab source code for the Master of Electronics and ICT course of Hardware Design at KU Leuven 2020-2021
ATE model trained on ACTER dataset (https://github.com/AylaRT/ACTER)
Approximately balanced estimation of average treatment effects in high dimensions.
Simulation of Benkeser D, Cai W, van der Laan MJ (2019+). A nonparametric super-efficient estimator of the average treatment effect.
A domain-aware automatic term extraction tool.
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.
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