Double Machine Learning for Static Panel Models with Fixed Effects

1 code implementation • 13 Dec 2023 • Paul Clarke, Annalivia Polselli

We develop three alternative approaches for handling unobserved individual heterogeneity based on extending the within-group estimator, first-difference estimator, and correlated random effect estimator (Mundlak, 1978) for non-linear models.

Robustness of Algorithms for Causal Structure Learning to Hyperparameter Choice

2 code implementations • 27 Oct 2023 • Damian Machlanski, Spyridon Samothrakis, Paul Clarke

We find that, while the choice of algorithm remains crucial to obtaining state-of-the-art performance, hyperparameter selection in ensemble settings strongly influences the choice of algorithm, in that a poor choice of hyperparameters can lead to analysts using algorithms which do not give state-of-the-art performance for their data.

Causal Discovery

Hyperparameter Tuning and Model Evaluation in Causal Effect Estimation

1 code implementation • 2 Mar 2023 • Damian Machlanski, Spyridon Samothrakis, Paul Clarke

We also find hyperparameter tuning and model evaluation are much more important than causal estimators and ML methods.

Causal Inference Model Selection

Undersmoothing Causal Estimators with Generative Trees

1 code implementation • 16 Mar 2022 • Damian Machlanski, Spyros Samothrakis, Paul Clarke

Inferring individualised treatment effects from observational data can unlock the potential for targeted interventions.