Nearest neighbor (NN) causal Inference for patient-center treatment effect

Music City Center 

When heterogeneous treatment is present, averaged treatment effect (ATE) is insufficient and even misleading for informing patient-centered treatment effect (PCTE). The ATE over nearest neighbors (NN) could offer a relative homogeneous sample that resembles the patient at the "center" of treatment decision. The k-nearest neighbor algorithm (kNN; Fix and Hodges, 1951) is a popular nonparametric method for supervised learning as it provides asymptotic uniform probability density over a given neighborhood (Silverman & Johns, 1989). The kNN has been used in matching based causal inference (Stuart, 2010). Applying kNN for identifying "alike" patients, Zhou and Kosorok (2017) shown the causal k-nearest neighbor (kNN) regime is universally consistent in the sense that it will learn the optimal treatment regime as the sample size increase. This study evaluates the leave-one-out (LOO) performances of some popular NN causal methods via simulation – kNN-ATE, kNN-IPTW, honest causal forest (HCF) and Bayesian adaptive regression tree (BART). The LOO performances are summarized by the quartiles of absolute error (Q1, Q3 and Median AE or MAE), root mean square predicted error (rMSPE) and averaged coverage rate (CR) over all LOO estimates compared against the oracle distributions for each LOO unit. Case study applied the methods to electronic health records to evaluate patient-centered treatment effect for children newly diagnosed with Juvenile Idiopathic Arthritis (JIA), a highly heterogeneous disease condition without known etiology. The study suggests the performances of all methods vary by the data density within NN, as well as its functional complexity of PCTE. Methods to evaluate and validate performances of PCTE method should look carefully into pairwise performances of LOO estimates. The NN causal is a useful approach to learn from real-world-data (RWD) about PCTE.

Heterogeneous treatment effect (HTE)

Averaged treatment effect (ATE)

patient-centered treatment effect (PCTE)

k-nearest neighbor (kNN)

honest causal forest (HCF)

Bayesian adaptive regression tree (BART)