Causal Inference, Perturbation & Interventional Designs — Hands-on

Learn how to move from correlation to causation in systems biology studies. This module covers causal diagrams, perturbation based designs and interventional analysis so that you can prioritise targets, combinations and experiments using a principled causal inference toolkit aligned with pathway and network models.

Causal Inference, Perturbation & Interventional Designs

Help Desk · WhatsApp

Session Index

Session 1 — Causal Thinking, DAGs & Confounding Session 2 — Perturbation Experiments & Interventional Design Session 3 — Estimating Causal Effects from Data Session 4 — Mini Capstone: Causal Network & Intervention Plan

Session 1

Fee: Rs 8800

Causal Thinking, DAGs & Confounding

From association to causation in biological systems

counterfactual intuition causal vs predictive models examples from omics studies

Causal diagrams and directed acyclic graphs (DAGs)

nodes, edges and paths confounders, mediators, colliders backdoor and frontdoor ideas

Bias structures and adjustment sets

confounding and selection bias minimal sufficient adjustment sets using DAG tools such as dagitty

Session 2

Fee: Rs 11800

Perturbation Experiments & Interventional Design

Randomised vs observational designs in systems biology

knockout and knockdown designs drug and ligand perturbation panels time series and dose response layouts

Mapping interventions onto pathways and networks

choosing nodes and edges to target single vs combination perturbations coverage vs cost trade offs

Practical design constraints and quality checks

replicates and randomisation blocking and batch considerations link to downstream modelling plans

Session 3

Fee: Rs 14800

Estimating Causal Effects from Data

Adjustment based estimators and weighting ideas

regression adjustment basics propensity scores and IPW intuition stratification and matching overview

Causal discovery and network structure hints from data

constraint based and score based ideas Granger style tests for time series cautious interpretation of learned edges

Tooling for causal analysis in practice

R packages such as dagitty and bnlearn Python libraries such as DoWhy / econml reproducible notebooks and reports

Session 4

Fee: Rs 18800

Mini Capstone: Causal Network & Intervention Plan

Build a small causal diagram for a pathway or network question

Theory + Practical

Estimate selected causal effects and prioritise interventions

effect of a target or combination sensitivity to adjustment choices shortlist of experiments or trials

Deliverables: DAG, code, tables and short methods note

diagram files or screenshots notebook with causal analysis assumptions and limitations summary

PDF

Causal Inference, Perturbation & Interventional Designs Training | Pathways, Networks & Experiments

Causal Inference, Perturbation & Interventional Designs — Hands-on