Estimating counterfactual outcome of different treatments from observational data is an important problem to assist decision making in a variety of fields. Among the various forms of treatment specification, bundle treatment has been widely adopted inmanyscenarios, such asrecommendation systems andonline marketing.

Estimating counterfactual outcome of different treatments from observational data is an important problem to assist decision making in a variety of fields. Among the various forms of treatment specification, bundle treatment has been widely adopted in many scenarios, such as recommendation systems and online marketing. The bundle treatment usually can be abstracted as a high dimensional binary vector, which makes it more challenging for researchers to remove the confounding bias in observational data. In this work, we assume the existence of low dimensional latent structure underlying bundle treatment. Via the learned latent representations of treatments, we propose a novel variational sample re-weighting (VSR) method to eliminate confounding bias by decorrelating the treatments and confounders. Finally, we conduct extensive experiments to demonstrate that the predictive model trained on this re-weighted dataset can achieve more accurate counterfactual outcome prediction.

Additional Feedback: As mentioned above, I think this method is very nice, but should be framed differently. In particular, the issue being addressed is not confounding _bias_; it is sample inefficiency when estimating the regression model f_{\theta_p}. This distinction is important in the causal inference literature, because a bias does not disappear with sample size. However, in this context, under the unconfoundedness assumption, if the model f_{\theta_p} is sufficiently flexible, it will converge to the same true counterfactual model in the large sample limit regardless of how the data are weighted (this is consistent with the experiments in the paper). In other words, the population risks E_{cf} and E_f w are minimized at the same function.

The authors propose a method for doing weighted sample adjustment for learning counterfactual regression models when treatments are high-dimensional. The reviewers, after some discussion, converged on the view that the paper is a nice contribution to the estimation theory for causal effects. One area where the paper could benefit is a discussion of the connections of the author's results to results on semi-parametric efficiency theory and influence functions (see e.g. It is likely there is a close relationship between the role weights play in improving efficiency and efficient influence functions for the problem (even under randomization).

Estimating counterfactual outcome of different treatments from observational data is an important problem to assist decision making in a variety of fields. Among the various forms of treatment specification, bundle treatment has been widely adopted in many scenarios, such as recommendation systems and online marketing. The bundle treatment usually can be abstracted as a high dimensional binary vector, which makes it more challenging for researchers to remove the confounding bias in observational data. In this work, we assume the existence of low dimensional latent structure underlying bundle treatment. Via the learned latent representations of treatments, we propose a novel variational sample re-weighting (VSR) method to eliminate confounding bias by decorrelating the treatments and confounders.

Causal inference plays an important role in explanatory analysis and decision making across various fields like statistics, marketing, health care, and education. Its main task is to estimate treatment effects and make intervention policies. Traditionally, most of the previous works typically focus on the binary treatment setting that there is only one treatment for a unit to adopt or not. However, in practice, the treatment can be much more complex, encompassing multi-valued, continuous, or bundle options. In this paper, we refer to these as complex treatments and systematically and comprehensively review the causal inference methods for addressing them. First, we formally revisit the problem definition, the basic assumptions, and their possible variations under specific conditions. Second, we sequentially review the related methods for multi-valued, continuous, and bundled treatment settings. In each situation, we tentatively divide the methods into two categories: those conforming to the unconfoundedness assumption and those violating it. Subsequently, we discuss the available datasets and open-source codes. Finally, we provide a brief summary of these works and suggest potential directions for future research.