Causal inference and machine learning are profoundly transforming the social sciences by enabling researchers to move beyond mere correlation to identify genuine cause-and-effect relationships and uncover complex patterns. This has led to more robust understandings of social phenomena, such as the impact of educational programmes on earnings and public health campaigns on disease rates, by credibly accounting for confounding factors or using other study designs. Machine learning complements this by efficiently handling the complexity and high dimensionality of large datasets. It can uncover subtle patterns and heterogeneous treatment effects, thereby generating individualised predictions.

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