Our technology
Causal inference is the process of identifying the true, independent effect of one factor within complex systems. Originally developed in economics to leverage observational
Causal inference
Originally developed in economics to leverage observational data where controlled experiments weren’t possible, causal inference techniques are now instrumental in biomedicine for uncovering disease biology from real-world genetic/phenotypic data.
Mendelian randomization as genetic instrumentation
Genetic variants can serve as natural instruments to estimate the effect of a modifiable exposure (e.g. gene expression level, biomarker) on a disease outcome, especially when direct estimation is confounded.
Mendelian randomization mimics randomized controlled trials by leveraging the (quasi-random) allocation of genes.
Our data & scope
Our pipeline uses these to run large-scale MR (Mendelian Randomization) analyses, systematically uncovering causal links between genes, biomarkers, and diseases.
These results enable target prioritization, biomarker selection, and patient stratification strategies.
Predictive model for clinical success
We developed a machine learning model built upon MR-derived features that predicts clinical success in Phase 2 trials, achieving a 75% hit rate in retrospective validation. This model is now integrated into our analytics stack and can be applied to new targets and pipelines.
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