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Ground-level ozone (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></semantics></math></inline-formula>) is a well-known atmospheric pollutant aside from particulate matter. China as a global populous country is facing serious surface <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></semantics></math></inline-formula> pollution. To detect the complex spatiotemporal transformation of the population exposure to ambient <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></semantics></math></inline-formula> pollution in China from 2005 to 2019, the Bayesian multi-stage spatiotemporal evolution hierarchy model was employed. To insight the drivers of the population exposure to ambient <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></semantics></math></inline-formula> pollution in China, a Bayesian spatiotemporal LASSO regression model (BST-LASSO-RM) and a spatiotemporal propensity score matching (STPSM) were firstly applied; then, a spatiotemporal causal inference method integrating the BST-LASSO-RM and STPSM was presented. The results show that the spatial pattern of the annual population-weighted ground-level <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></semantics></math></inline-formula> (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><mi>W</mi><mi>G</mi><mi>L</mi><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></semantics></math></inline-formula>) concentrations, representing population exposure to ambient <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></semantics></math></inline-formula>, in China has transformed since 2014. Most regions (72.2%) experienced a decreasing trend in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><mi>W</mi><mi>G</mi><mi>L</mi><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></semantics></math></inline-formula> pollution in the early stage, but in the late stage, most areas (79.3%) underwent an increasing trend. Some drivers on <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><mi>W</mi><mi>G</mi><mi>L</mi><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></semantics></math></inline-formula> concentrations have partial spatial spillover effects. The <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><mi>W</mi><mi>G</mi><mi>L</mi><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></semantics></math></inline-formula> concentrations in a region can be driven by this region’s surrounding areas’ economic factors, wind speed, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><mi>W</mi><mi>G</mi><mi>L</mi><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></semantics></math></inline-formula> concentrations. The major drivers with six local factors in 2005–2014 changed to five local factors and one spatial adjacent factor in 2015–2019. The driving of the traffic and green factors have no spatial spillover effects. Three traffic factors showed a negative driving effect in the early stage, but only one, bus ridership per capita (BRPC), retains the negative driving effect in the late stage. The factor with the maximum driving contribution is BRPC in the early stage, but <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><msub><mrow><mi>M</mi></mrow><mrow><mn>2.5</mn></mrow></msub></mrow></semantics></math></inline-formula> pollution in the late stage, and the corresponding driving contribution is 17.57%. Green area per capita and urban green coverage rates have positive driving effects. The driving effects of the climate factors intensified from the early to the later stage.