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Found 5 papers, 1 papers with code
Variable Selection for Causal Inference via Outcome-Adaptive Random Forest
We propose the outcome-adaptive random forest (OARF) that only includes desirable variables for estimating the propensity score to decrease bias and variance.
CATE meets ML -- The Conditional Average Treatment Effect and Machine Learning
For treatment effects - one of the core issues in modern econometric analysis - prediction and estimation are two sides of the same coin.
Making experimental data tables in the life sciences more FAIR: a pragmatic approach
Making data compliant with the FAIR Data principles (Findable, Accessible, Interoperable, Reusable) is still a challenge for many researchers, who are not sure which criteria should be met first and how.
Group Average Treatment Effects for Observational Studies
To control for confounding in the linear model, we use Neyman-orthogonal moments to partial out the effect that covariates have on both, the treatment assignment and the outcome.
Affordable Uplift: Supervised Randomization in Controlled Experiments
To increase the cost-efficiency of experimentation and facilitate frequent data collection and model training, we introduce supervised randomization.
