Doubly robust identification of treatment effects from multiple environments
Piersilvio De Bartolomeis Julia Kostin Javier Abad Yixin Wang Fanny Yang
Practical and ethical constraints often require the use of observational data for causal inference, particularly in medicine and social sciences. Yet, observational datasets are prone to confounding, potentially compromising the validity of causal conclusions. 
 While it is possible to correct for biases if the underlying causal graph is known, this is rarely a feasible ask in practical scenarios. A common strategy is to adjust for all available covariates, yet this approach can yield biased treatment effect estimates, especially when post-treatment or unobserved variables are present.
We propose RAMEN, an algorithm that produces unbiased treatment effect estimates
by leveraging the heterogeneity of multiple data sources without the need to know or learn the underlying causal graph. Notably, RAMEN achieves *doubly robust identification*: it can identify the treatment effect whenever 
the causal parents of the treatment or those of the outcome are observed, and the node whose parents are observed satisfies an invariance assumption. Empirical evaluations across synthetic, semi-synthetic, and real-world datasets show that our approach significantly outperforms existing methods.
treatment effect, confounding, heterogenous data, causality, causal inference, unobserved variables, post-treatment variables, collider bias