6,480 research outputs found
Noisy-OR Models with Latent Confounding
Given a set of experiments in which varying subsets of observed variables are
subject to intervention, we consider the problem of identifiability of causal
models exhibiting latent confounding. While identifiability is trivial when
each experiment intervenes on a large number of variables, the situation is
more complicated when only one or a few variables are subject to intervention
per experiment. For linear causal models with latent variables Hyttinen et al.
(2010) gave precise conditions for when such data are sufficient to identify
the full model. While their result cannot be extended to discrete-valued
variables with arbitrary cause-effect relationships, we show that a similar
result can be obtained for the class of causal models whose conditional
probability distributions are restricted to a `noisy-OR' parameterization. We
further show that identification is preserved under an extension of the model
that allows for negative influences, and present learning algorithms that we
test for accuracy, scalability and robustness
Probabilistic Matching: Causal Inference under Measurement Errors
The abundance of data produced daily from large variety of sources has
boosted the need of novel approaches on causal inference analysis from
observational data. Observational data often contain noisy or missing entries.
Moreover, causal inference studies may require unobserved high-level
information which needs to be inferred from other observed attributes. In such
cases, inaccuracies of the applied inference methods will result in noisy
outputs. In this study, we propose a novel approach for causal inference when
one or more key variables are noisy. Our method utilizes the knowledge about
the uncertainty of the real values of key variables in order to reduce the bias
induced by noisy measurements. We evaluate our approach in comparison with
existing methods both on simulated and real scenarios and we demonstrate that
our method reduces the bias and avoids false causal inference conclusions in
most cases.Comment: In Proceedings of International Joint Conference Of Neural Networks
(IJCNN) 201
Causal Effect Inference with Deep Latent-Variable Models
Learning individual-level causal effects from observational data, such as
inferring the most effective medication for a specific patient, is a problem of
growing importance for policy makers. The most important aspect of inferring
causal effects from observational data is the handling of confounders, factors
that affect both an intervention and its outcome. A carefully designed
observational study attempts to measure all important confounders. However,
even if one does not have direct access to all confounders, there may exist
noisy and uncertain measurement of proxies for confounders. We build on recent
advances in latent variable modeling to simultaneously estimate the unknown
latent space summarizing the confounders and the causal effect. Our method is
based on Variational Autoencoders (VAE) which follow the causal structure of
inference with proxies. We show our method is significantly more robust than
existing methods, and matches the state-of-the-art on previous benchmarks
focused on individual treatment effects.Comment: Published as a conference paper at NIPS 201
The effect of latent confounding processes on the estimation of the strength of causal influences in chain-type networks
The authors acknowledge GTD TauRx Therapeutics centres for generous funding of this research.Peer reviewedPublisher PD
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