AI Has A Data Problem - Causal Data May Solve It

Most AI systems are trained on historical data. When conditions shift due to changing consumer sentiment, models trained on ...
ascopubs.org

Causal Inference in Oncology Comparative Effectiveness Research Using Observational Data: Are Instrumental Variables Underutilized?

In the article that accompanies this editorial, Lu et al 5 conducted a systematic review on the use of instrumental variable (IV) methods in oncology comparative effectiveness research. The main ...
BMJ

Causal inference in physical activity epidemiology research: an opportunity for better evidence

Decades of research have established a significant link between physical activity and health, influencing agenda setting, policy making and community awareness.1–4 However, the field continues to ...
lse

Causal Inference for Observational and Experimental Studies

This course is available on the MSc in Applied Social Data Science, MSc in Behavioural Science, MSc in Human Geography and Urban Studies (Research), MSc in Innovation Policy, MSc in International ...
AlphaGalileo

Bridging Logical Gaps: Causal Inference for Robust Multi-Hop QA

Sub-headline: BIT researchers introduce CausalBridgeQA to tackle spurious correlations in complex multi-hop reasoning chains.

Why Crypto Markets Need Causal AI — And Why Statistical Models Keep Failing Them

The feedback loops that define DeFi, on-chain contagion, and crypto financial crime are not statistical phenomena. They are ...

Beyond Prediction: Why DeFi Risk and Crypto Market Transmission Demand Causal Fuzzy Cognitive Maps

The crypto market has defeated more prediction models than any other asset class in history. Neural networks trained on ...
lse

Causal Inference for Observational and Experimental Studies

This course is available on the MPhil/PhD in Economic Geography, MPhil/PhD in Environmental Economics, MPhil/PhD in International Relations, MPhil/PhD in Regional and Urban Planning Studies, MRes in ...