MultiMBNN: Matched and Balanced Causal Inference with Neural Networks

Causal inference (CI) in observational studies has received a lot of attention in healthcare, education, ad attribution, policy evaluation, etc. Confounding is a typical hazard, where the context affects both, the treatment assignment and response. In a multiple treatment scenario, we propose the neural network based MultiMBNN, where we overcome confounding by employing generalized propensity score based matching, and learning balanced representations. We benchmark the performance on synthetic and real-world datasets using PEHE, and mean absolute percentage error over ATE as metrics. MultiMBNN outperforms the state-of-the-art algorithms for CI such as TARNet and Perfect Match (PM).

[1]  Max Welling,et al.  Causal Effect Inference with Deep Latent-Variable Models , 2017, NIPS 2017.

[2]  Elizabeth A Stuart,et al.  Matching methods for causal inference: A review and a look forward. , 2010, Statistical science : a review journal of the Institute of Mathematical Statistics.

[3]  Mihaela van der Schaar,et al.  Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes , 2017, NIPS.

[4]  Lovekesh Vig,et al.  MetaCI: Meta-Learning for Causal Inference in a Heterogeneous Population , 2019, ArXiv.

[5]  Susan Athey,et al.  Machine Learning and Causal Inference for Policy Evaluation , 2015, KDD.

[6]  Richard A. Nielsen,et al.  Why Propensity Scores Should Not Be Used for Matching , 2019, Political Analysis.

[7]  Uri Shalit,et al.  Learning Representations for Counterfactual Inference , 2016, ICML.

[8]  Walter Karlen,et al.  Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks , 2018, ArXiv.

[9]  Kosuke Imai,et al.  Causal Inference With General Treatment Regimes , 2004 .

[10]  Uri Shalit,et al.  Estimating individual treatment effect: generalization bounds and algorithms , 2016, ICML.

[11]  Joaquin Quiñonero Candela,et al.  Counterfactual reasoning and learning systems: the example of computational advertising , 2013, J. Mach. Learn. Res..

[12]  Mihaela van der Schaar,et al.  GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets , 2018, ICLR.

[13]  Jian Yang,et al.  Causal Inference via Sparse Additive Models with Application to Online Advertising , 2015, AAAI.