Improving identification algorithms in causal inference
Causal models provide a formal approach to the study of causality. One of the most useful
features of causal modeling is that it enables one to make causal claims about a phenomenon
using observational data alone under suitable conditions. This feature enables the analysis
of interventions that may be infeasible to conduct in the real world for practical or ethical
reasons. The uncertainty associated with the variables of interest is taken into account by
including a probability distribution in the causal model, making it is possible to study the
effects of external interventions by examining how this distribution is changed by the action.
The probability distribution of a specific variable in a causal model perturbed by an outside
intervention is the causal effect of that intervention on the variable.
One of the most fundamental problems of causal inference is determining whether a causal
effect can be uniquely expressed in terms of the joint probability distribution over the observed
variables in a given causal model. Causal effects that can be expressed in this way are called
identifiable and they serve as the link between observational and experimental information.
Complete solutions to the identifiability problem take the form of an algorithm that produces an
expression in terms of observed quantities whenever the causal effect given as input is identifiable.
However, completeness in this context refers only to the correctness and exhaustiveness of the
methods. The formulas obtained as output from identifiability algorithms are often impractical
and unnecessarily complicated.
The thesis augments the pre-existing identifiability methodology by providing a simplification procedure that drastically improves the complicated outputs in many cases. Simplification
also has practical benefits when statistical estimation is considered if variables affected by bias
or missing data no longer appear in the simplified expression. The thesis also introduces a
new method called pruning, which aims to eliminate variables that are unnecessary for the
identification task from the causal model itself. Finally, a variety of identification algorithms
are implemented more complicated settings, such as when data are available from multiple
domains. The methods are provided through the R package “causaleffect”
...
Publisher
University of JyväskyläISBN
978-951-39-7519-7ISSN Search the Publication Forum
1457-8905Metadata
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