57,815 research outputs found
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
Federated Causal Inference in Heterogeneous Observational Data
We are interested in estimating the effect of a treatment applied to
individuals at multiple sites, where data is stored locally for each site. Due
to privacy constraints, individual-level data cannot be shared across sites;
the sites may also have heterogeneous populations and treatment assignment
mechanisms. Motivated by these considerations, we develop federated methods to
draw inference on the average treatment effects of combined data across sites.
Our methods first compute summary statistics locally using propensity scores
and then aggregate these statistics across sites to obtain point and variance
estimators of average treatment effects. We show that these estimators are
consistent and asymptotically normal. To achieve these asymptotic properties,
we find that the aggregation schemes need to account for the heterogeneity in
treatment assignments and in outcomes across sites. We demonstrate the validity
of our federated methods through a comparative study of two large medical
claims databases
Causal statistical inference in high dimensions
We present a short selective review of causal inference from observational data, with a particular emphasis on the high-dimensional scenario where the number of measured variables may be much larger than sample size. Despite major identifiability problems, making causal inference from observational data very ill-posed, we outline a methodology providing useful bounds for causal effects. Furthermore, we discuss open problems in optimization, non-linear estimation and for assigning statistical measures of uncertainty, and we illustrate the benefits and limitations of high-dimensional causal inference for biological application
Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles
While feedback loops are known to play important roles in many complex
systems, their existence is ignored in a large part of the causal discovery
literature, as systems are typically assumed to be acyclic from the outset.
When applying causal discovery algorithms designed for the acyclic setting on
data generated by a system that involves feedback, one would not expect to
obtain correct results. In this work, we show that---surprisingly---the output
of the Fast Causal Inference (FCI) algorithm is correct if it is applied to
observational data generated by a system that involves feedback. More
specifically, we prove that for observational data generated by a simple and
-faithful Structural Causal Model (SCM), FCI is sound and complete, and
can be used to consistently estimate (i) the presence and absence of causal
relations, (ii) the presence and absence of direct causal relations, (iii) the
absence of confounders, and (iv) the absence of specific cycles in the causal
graph of the SCM. We extend these results to constraint-based causal discovery
algorithms that exploit certain forms of background knowledge, including the
causally sufficient setting (e.g., the PC algorithm) and the Joint Causal
Inference setting (e.g., the FCI-JCI algorithm).Comment: Major revision. To appear in Proceedings of the 36 th Conference on
Uncertainty in Artificial Intelligence (UAI), PMLR volume 124, 202
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