On assumption-free tests and confidence intervals for causal effects estimated by machine learning.
Lin LiuRajarshi MukherjeeJames M. RobinsPublished in: CoRR (2019)
Keyphrases
- confidence intervals
- machine learning
- causal effects
- sample size
- structural equation models
- markov chain
- stochastic systems
- causal relations
- observational data
- monte carlo
- linear models
- test set
- data mining
- model selection
- information extraction
- learning algorithm
- conditional probabilities
- causal relationships
- text mining
- supervised learning
- stopping rules
- semi supervised learning
- text classification
- roc curve
- least squares
- active learning
- feature selection
- information retrieval