Taking causal inference to the extreme!
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Updated
May 12, 2024 - Julia
Taking causal inference to the extreme!
A pure Julia implementation of the Targeted Minimum Loss-based Estimation
Fast Inference of Biological Networks from Directed Regulations (Findr) in Julia
Contains the LLCB method from Weinstock* and Arce* et al., 2023
A suite of Julia packages for difference-in-differences
Causal inference, graphical models and structure learning in Julia
Source code for the paper "Lifted Causal Inference in Relational Domains" (CLeaR 2024)
Algorithms for detecting associations, dynamical influences and causal inference from data.
Synthetic difference in differences - Julia implementation of https://synth-inference.github.io/synthdid/
Causal Inference using Gaussian Processes with Structured Latent Confounders. Estimate treatment effects with Gaussian processes.
Regression-based multi-period difference-in-differences with heterogenous treatment effects
Base package for DiffinDiffs.jl
Causal Inference with Invariant Prediction
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