7,600 research outputs found
Confounding Equivalence in Causal Inference
The paper provides a simple test for deciding, from a given causal diagram,
whether two sets of variables have the same bias-reducing potential under
adjustment. The test requires that one of the following two conditions holds:
either (1) both sets are admissible (i.e., satisfy the back-door criterion) or
(2) the Markov boundaries surrounding the manipulated variable(s) are identical
in both sets. Applications to covariate selection and model testing are
discussed.Comment: Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty
in Artificial Intelligence (UAI2010
Learning Joint Nonlinear Effects from Single-variable Interventions in the Presence of Hidden Confounders
We propose an approach to estimate the effect of multiple simultaneous
interventions in the presence of hidden confounders. To overcome the problem of
hidden confounding, we consider the setting where we have access to not only
the observational data but also sets of single-variable interventions in which
each of the treatment variables is intervened on separately. We prove
identifiability under the assumption that the data is generated from a
nonlinear continuous structural causal model with additive Gaussian noise. In
addition, we propose a simple parameter estimation method by pooling all the
data from different regimes and jointly maximizing the combined likelihood. We
also conduct comprehensive experiments to verify the identifiability result as
well as to compare the performance of our approach against a baseline on both
synthetic and real-world data.Comment: Accepted to The Conference on Uncertainty in Artificial Intelligence
(UAI) 202
Robust causal structure learning with some hidden variables
We introduce a new method to estimate the Markov equivalence class of a
directed acyclic graph (DAG) in the presence of hidden variables, in settings
where the underlying DAG among the observed variables is sparse, and there are
a few hidden variables that have a direct effect on many of the observed ones.
Building on the so-called low rank plus sparse framework, we suggest a
two-stage approach which first removes the effect of the hidden variables, and
then estimates the Markov equivalence class of the underlying DAG under the
assumption that there are no remaining hidden variables. This approach is
consistent in certain high-dimensional regimes and performs favourably when
compared to the state of the art, both in terms of graphical structure recovery
and total causal effect estimation
We Are Not Your Real Parents: Telling Causal from Confounded using MDL
Given data over variables we consider the problem of finding out whether jointly causes or whether they are all confounded by an unobserved latent variable . To do so, we take an information-theoretic approach based on Kolmogorov complexity. In a nutshell, we follow the postulate that first encoding the true cause, and then the effects given that cause, results in a shorter description than any other encoding of the observed variables. The ideal score is not computable, and hence we have to approximate it. We propose to do so using the Minimum Description Length (MDL) principle. We compare the MDL scores under the models where causes and where there exists a latent variables confounding both and and show our scores are consistent. To find potential confounders we propose using latent factor modeling, in particular, probabilistic PCA (PPCA). Empirical evaluation on both synthetic and real-world data shows that our method, CoCa, performs very well -- even when the true generating process of the data is far from the assumptions made by the models we use. Moreover, it is robust as its accuracy goes hand in hand with its confidence
Learning Adjustment Sets from Observational and Limited Experimental Data
Estimating causal effects from observational data is not always possible due
to confounding. Identifying a set of appropriate covariates (adjustment set)
and adjusting for their influence can remove confounding bias; however, such a
set is typically not identifiable from observational data alone. Experimental
data do not have confounding bias, but are typically limited in sample size and
can therefore yield imprecise estimates. Furthermore, experimental data often
include a limited set of covariates, and therefore provide limited insight into
the causal structure of the underlying system. In this work we introduce a
method that combines large observational and limited experimental data to
identify adjustment sets and improve the estimation of causal effects. The
method identifies an adjustment set (if possible) by calculating the marginal
likelihood for the experimental data given observationally-derived prior
probabilities of potential adjustmen sets. In this way, the method can make
inferences that are not possible using only the conditional dependencies and
independencies in all the observational and experimental data. We show that the
method successfully identifies adjustment sets and improves causal effect
estimation in simulated data, and it can sometimes make additional inferences
when compared to state-of-the-art methods for combining experimental and
observational data.Comment: 10 pages, 5 figure
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