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README.md

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 # Benchmarking Mendelian Randomization methods for causal inference using genome‐wide association study summary statistics
 ## The experimental design for benchmarking MR methods
 We present a benchmarking analysis of MR methods for causal inference with real-world genetic datasets. Our focus is on MR methods that utilize GWAS summary statistics as input, as they do not require access to individual-level GWAS data and are widely applicable. Specifically, we consider 15 MR methods, including the standard IVW (fixed) and IVW (random) and 13 other advanced MR methods: Egger, RAPS, Weighted-median, Weighted-mode, MR-PRESSO, MRMix, cML-MA, MR-Robust, MR-Lasso, MR-CUE, CAUSE, MRAPSS and MR-ConMix (Figure A). The procedure for running the MR methods is outlined in Figure B. To assess the performance of these MR methods, we utilized real-world datasets and focused on three key aspects: type I error control, the accuracy of causal effect estimates, replicability and power (Figure C).
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 ## Datasets
 The five datasets used in the MR benchmarking study can be downloaded here.