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