Enterprise Causal AI/ML.
Moving beyond predictions.
Using Real-World Evidence to Measure & Improve KPIs
Causal AI / ML is one of the most exciting new advancements in analytics. CML Insight helps organizations measure and improve their critical KPIs.
We work with educational institutions, government, edtech companies, fintech and healthcare providers to discover the incredible insights that remain hidden in data.
Learn more about Causal AI/ML
What is CML Insight?
CML Insight is an advanced data science product and services company designed to help e organizations measure the impact and efficacy of their initiatives, programs and products to improve outcomes. Using advances in Causal AI/ML our highly experienced data scientists and machine learning experts leverage novel approaches to uncover the causal relationships in your data.
We provide insights using your historical information, and more importantly we help you run experiments to continuously improve the organization against critical KPIs.
We develop an initial set of insights using your historical data set. We then focus on your KPIs, the ones you are trying to improve and have the highest effect on solving your operational problems or student success challenges. We provide specific evidence-based recommendations so you don’t have to guess and we track the efficacy of those experiments over time.
Using Causal ML, we leverage a defined set of complementary ML algorithms with a focus on improving business KPIs or student success, not just on predicting risk.
CML Insight delivers a specific map of your initiatives, interventions, investments ranked to better understand which streams of work have the highest impact potential. This allows you and your support team to better optimize your portfolio of work to serve your diverse students and users. We also help you run play-based experiments over time to continually refine and improve the efficacy of your interventions and products to maximize outcomes.
Recent Blog Articles
Published Research & Causal AI / ML Articles
“Where causation is concerned, a grain of wise subjectivity tells us more about the real world than any amount of objectivity.”
Judea Pearl, The Book of Why: The New Science of Cause and Effect
Creating impact using Real-World Evidence (RWE)
New advances in Causal AI / ML allows us to go beyond basic “pattern-matching” and “curve fitting” typically used over the past decade via predictive analytics.
Causal AI / ML gives us the ability to learn cause-and-effect relationships; the capability to simulate and measure impact of interventions; and allows us the ability to imagine what-ifs beyond the limits of historical data.
I founded CML Insight to democratize machine learning insights to help all organizations improve the way they operate and drive meaningful outcomes.