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Matching on Balanced Nonlinear Representations for Treatment Effects Estimation

NeurIPS 2017 · Probabilistic and scientific modeling › Treatment effects · approach: Benchmarking treatment effect models · 36 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Compare BNR-NNM against RNNM, PCA-NNM, LPP-NNM, PSM, Eud/Mah-NNM across dimensions 2-100BNR-NNM · Synthetic (N=1000, d=100)MSE of ATTHelped
Vary matching dimension; best at 5 (ablation)BNR-NNM · SyntheticMSEMixed
Use BNR-NNM instead of RNNMBNR-NNM · IHDPepsilon_ATT: 0.16±0.07 -> 0.16±0.06No clear effect
Use BNR-NNM instead of Eu-NNMBNR-NNM · IHDPepsilon_ATT: 0.18±0.06 -> 0.16±0.06Helped
Use BNR-NNM instead of Mah-NNMBNR-NNM · IHDPepsilon_ATT: 0.31±0.12 -> 0.16±0.06Helped
Use BNR-NNM instead of PCA-NNMBNR-NNM · IHDPepsilon_ATT: 0.19±0.11 -> 0.16±0.06Helped
Use BNR-NNM instead of PSMBNR-NNM · IHDPepsilon_ATT: 0.26±0.08 -> 0.16±0.06Helped
Use BNR-NNM instead of Euclidean NNMBNR-NNM · LaLondeATT bias: 164% -> 12%Helped
Use BNR-NNM instead of PSMBNR-NNM · LaLondeATT bias: 201% -> 12%Helped
Use BNR-NNM instead of RNNMBNR-NNM · LaLondeATT bias: 163% -> 12%Helped
Use BNR-NNM instead of CBPS (balance-aware baseline)BNR-NNM · LaLondeATT bias: 52% -> 12%Helped
Use BNR-NNM instead of DNN counterfactual regressionBNR-NNM · LaLondeATT bias: 16% -> 12%Helped

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