CausalKit™ is a no-code platform that empowers teams to run advanced predictive modeling and causal inference using cutting-edge Bayesian and machine learning methods. Whether you're forecasting future outcomes or evaluating the true impact of interventions in A/B tests and randomized controlled trials, CausalKit helps you make smarter, evidence-based decisions with clear and credible insights. Users can upload data from CSV, Excel, SQL, or via API, and choose between two main workflows: predictive modeling and causal inference. The platform automatically generates and deploys models as secure REST API endpoints, making it easy to generate real-time forecasts or estimate treatment effects without writing code. CausalKit includes an intuitive visual workflow builder, seamless API integration, and secure data management using AWS S3. All data is encrypted, with the option to auto-delete datasets after training, preserving only the final model. The platform supports usage-based pricing and offers discounts for academic and nonprofit users. From forecasting conversion rates to measuring the effect of price changes or marketing campaigns, CausalKit transforms how organizations analyze data—delivering fast, reproducible, and transparent analysis with credible intervals between 95% and 99%.