CausalKit

Causal discovery, counterfactual inference, and robust modeling

Product category: Causal modeling

Applicable platforms: Python

Technical affiliation: Causal Discovery, DoWhy, EconML

Programming language affiliation: Python

Tags: #CausalML #Inference Product

Type: Causal modeling

CausalKit is used to identify causation from correlation: DAG learning, instrumental variables, propensity score matching, double robust estimation, and counterfactual simulation, evaluating marginal effects of interventions on outcomes. For time series and multimodal projects, it can produce more robust features (reducing performance collapse under distribution drifts); for policy/decision, it can evaluate counterfactual returns of "what if not taking this action", aiding reward and constraint design. It engineers causal thinking, letting models not only "fit data", but "understand mechanisms".

Delivery method: Instant digital download after purchase
License: Single-user commercial license
Usage limit: One-time use
Support: Technical documentation provided in the delivery file; no human technical support included