Interests
My primary research interests are Causality and Machine Learning (ML), with an emphasis on using modern ML methods to solve causal problems. More specific areas of interest are as follows.
Causality
- Treatment Effect Estimation
- Structure Learning / Causal Discovery
- Observational Data
- Cross-sectional and Panel Data
- Treatment Recommendation / Policy Learning
Machine Learning
- Hyperparameter Tuning
- Model Selection and Evaluation
- Covariate Shift
- Domain Adaptation
- Grokking
Working Papers
- D. Machlanski, S. Samothrakis, and P. Clarke, ‘Robustness of Algorithms for Causal Structure Learning to Hyperparameter Choice’. arXiv, Oct. 27, 2023. doi: 10.48550/arXiv.2310.18212. [link] (accepted for CLeaR 2024)
- D. Machlanski, S. Samothrakis, and P. Clarke, ‘Hyperparameter Tuning and Model Evaluation in Causal Effect Estimation’. arXiv, Mar. 02, 2023. doi: 10.48550/arXiv.2303.01412. [link]
- D. Machlanski, S. Samothrakis, and P. Clarke, ‘Undersmoothing Causal Estimators with Generative Trees’. arXiv, Mar. 16, 2022. doi: 10.48550/arXiv.2203.08570. [link]
Presentations
- The Importance of Hyperparameter Tuning in Causal Effect Estimation, Causal Data Science Meeting 2022 [slides].
- Causal Discovery for Treatment Effect Estimation from Observational Data, MiSoC Annual Workshop 2021.
Code
- For the paper ‘Robustness of Algorithms for Causal Structure Learning to Hyperparameter Choice’ [github]
- For the paper ‘Hyperparameter Tuning and Model Evaluation in Causal Effect Estimation’ [github]
- For the paper ‘Undersmoothing Causal Estimators with Generative Trees’ [github]
- CATE Benchmark [github]