End-To-End Causal Effect Estimation from Unstructured Natural Language Data

Neural Information Processing Systems 

Knowing the effect of an intervention is critical for human decision-making, but current approaches for causal effect estimation rely on manual data collection and structuring, regardless of the causal assumptions.