84 research outputs found
Local search for efficient causal effect estimation
Causal effect estimation from observational data is an important but
challenging problem. Causal effect estimation with unobserved variables in data
is even more difficult. The challenges lie in (1) whether the causal effect can
be estimated from observational data (identifiability); (2) accuracy of
estimation (unbiasedness), and (3) fast data-driven algorithm for the
estimation (efficiency). Each of the above problems by its own, is challenging.
There does not exist many data-driven methods for causal effect estimation so
far, and they solve one or two of the above problems, but not all. In this
paper, we present an algorithm that is fast, unbiased and is able to confirm if
a causal effect is identifiable or not under a very practical and commonly seen
problem setting. To achieve high efficiency, we approach the causal effect
estimation problem as a local search for the minimal adjustment variable sets
in data. We have shown that identifiability and unbiased estimation can be both
resolved using data in our problem setting, and we have developed theorems to
support the local search for searching for adjustment variable sets to achieve
unbiased causal effect estimation. We make use of frequent pattern mining
strategy to further speed up the search process. Experiments performed on an
extensive collection of synthetic and real-world datasets demonstrate that the
proposed algorithm outperforms the state-of-the-art causal effect estimation
methods in both accuracy and time-efficiency.Comment: 30 page
Identify treatment effect patterns for personalised decisions
In personalised decision making, evidence is required to determine suitable
actions for individuals. Such evidence can be obtained by identifying treatment
effect heterogeneity in different subgroups of the population. In this paper,
we design a new type of pattern, treatment effect pattern to represent and
discover treatment effect heterogeneity from data for determining whether a
treatment will work for an individual or not. Our purpose is to use the
computational power to find the most specific and relevant conditions for
individuals with respect to a treatment or an action to assist with
personalised decision making. Most existing work on identifying treatment
effect heterogeneity takes a top-down or partitioning based approach to search
for subgroups with heterogeneous treatment effects. We propose a bottom-up
generalisation algorithm to obtain the most specific patterns that fit
individual circumstances the best for personalised decision making. For the
generalisation, we follow a consistency driven strategy to maintain inner-group
homogeneity and inter-group heterogeneity of treatment effects. We also employ
graphical causal modelling technique to identify adjustment variables for
reliable treatment effect pattern discovery. Our method can find the treatment
effect patterns reliably as validated by the experiments. The method is faster
than the two existing machine learning methods for heterogeneous treatment
effect identification and it produces subgroups with higher inner-group
treatment effect homogeneity
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