A Survey on Causal Inference <s> Multitask Learning Methods <s> We propose a novel approach for inferring the individualized causal effects of a treatment (intervention) from observational data. Our approach conceptualizes causal inference as a multitask learning problem; we model a subject's potential outcomes using a deep multitask network with a set of shared layers among the factual and counterfactual outcomes, and a set of outcome-specific layers. The impact of selection bias in the observational data is alleviated via a propensity-dropout regularization scheme, in which the network is thinned for every training example via a dropout probability that depends on the associated propensity score. The network is trained in alternating phases, where in each phase we use the training examples of one of the two potential outcomes (treated and control populations) to update the weights of the shared layers and the respective outcome-specific layers. Experiments conducted on data based on a real-world observational study show that our algorithm outperforms the state-of-the-art. <s> BIB001 </s> A Survey on Causal Inference <s> Multitask Learning Methods <s> Estimating what would be an individual's potential response to varying levels of exposure to a treatment is of high practical relevance for several important fields, such as healthcare, economics and public policy. However, existing methods for learning to estimate counterfactual outcomes from observational data are either focused on estimating average dose-response curves, or limited to settings with only two treatments that do not have an associated dosage parameter. Here, we present a novel machine-learning approach towards learning counterfactual representations for estimating individual dose-response curves for any number of treatments with continuous dosage parameters with neural networks. Building on the established potential outcomes framework, we introduce performance metrics, model selection criteria, model architectures, and open benchmarks for estimating individual dose-response curves. Our experiments show that the methods developed in this work set a new state-of-the-art in estimating individual dose-response. <s> BIB002
Treatment group and control group always share some common features except for their idiosyncratic characteristics. Naturally, causal inference can be conceptualized as a multitask learning problem with a set of shared layers for treated group and control group together, and a set of specific layers for treated group and control group separately. The impact of selection bias in multi-task learning problem can be alleviated via a propensity-dropout regularization scheme BIB001 , in which the network is thinned for every training example via a dropout probability that depends on the associated propensity score. The dropout probability is higher for subjects with features that belong in a region of poor overlap in the feature space between treatment and control group. The Bayesian method also can be extended under multi-task model. A nonparametric Bayesian method [3] uses a multi-task Gaussian process with a linear coregionalization kernel as a prior over the vector-valued reproducing kernel Hilbert space. The Bayesian approach allows computing individualized measures of confidence in our estimates via pointwise credible intervals, which are crucial for realizing the full potential of precision medicine. The impact of selection bias is alleviated via a risk-based empirical Bayes method for adapting the multi-task GP prior, which jointly minimizes the empirical error in factual outcomes and the uncertainty in counterfactual outcomes. The multi-task model can be extended to multiple treatments even with continuous parameters in each treatment. The dose response network (DRNet) architecture BIB002 with shared base layers, N W intermediary treatment layers, and N W × E heads for the multiple treatment setting with an associated dosage parameter s. The shared base layers are trained on all samples, and the treatment layers are only trained on samples from their respective treatment category. Each treatment layer is further subdivided into E head layers. Each head layer is assigned a dosage stratum that subdivides the range of potential dosages