Authors - Md. Shahidul Islam, Ronobir Chandra Sarker Abstract - The widespread adoption of artificial intelligence (AI) and automation is emerging as a central driver of productivity growth in European firms. Yet identifying the causal impact of AI adoption on firm productivity is complicated by endogeneity, selection bias, and heterogeneous treatment effects. This paper analyzes the productivity effects of AI and automation adoption using a unified framework that combines traditional econometric techniques with causal machine learning methods. Using firm-level data from Orbis merged with industry-level productivity and ICT capital measures from EU KLEMS for the period 2010–2023, we estimate both average and heterogeneous treatment effects. Double Machine Learning yields a robust average productivity gain of approximately 4.5 percent, while Causal Forests reveal substantial heterogeneity across industries, firm size, human capital, and digital maturity. The results provide credible causal evidence that AI adoption enhances firm productivity and highlight the importance of complementary capabilities in realizing its economic benefits.
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