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In ecology, there is a growing need to move beyond correlations to uncovering causal effects from observational data. With the parallel increase in big data and machine learning algorithms, the opportunity now exists to benefit from causal machine learning methodologies. This paper presents an accessible overview of four causal machine learning methods, double machine learning (DML), targeted maximum likelihood estimation (TMLE), deep instrumental variables (Deep IV) and causal forests, that can be applied across ecological contexts. DML and TMLE leverage machine learning to estimate causal effects in the presence of known confounders. Deep IV offers a robust solution for addressing unmeasured confounding or bidirectional relationships by pairing valid instruments with deep neural networks. Causal forests uncover heterogeneity in causal effects, shedding light on context‐dependent ecological responses. Adding these causal machine learning techniques to an ecologist's broader causal toolkit will increase the options researchers have for estimating causal relationships, particularly when dealing with complex and large‐scale observational data.

The advancement of the digital economy is vital for decreasing agricultural carbon emissions and fostering high-quality agricultural development. Using panel data from 31 Chinese provinces between 2000 and 2021, this paper employs a dual machine learning model for causal inference to analyze the impact of digital financial inclusion on agricultural carbon emissions intensity, its underlying mechanisms, and the characteristics of heterogeneity. The study finds that digital inclusive finance significantly reduces agricultural carbon intensity through two main channels: enhancing scientific and technological innovation and narrowing the urban-rural income gap. Additionally, the expansion of arable land management and the acceleration of economic structural transformation positively moderate these effects. These conclusions remain robust after a series of robustness tests. Further combining factors such as resource endowment, geographic location, economic concentration, and food production areas in the heterogeneity test, the study found that regional differences significantly influence the effect of financial inclusion on agricultural carbon intensity. Therefore, it is essential to enhance the development of inclusive finance, break down regional barriers to promote synergistic development, actively support economic transformation and large-scale operations, strengthen scientific and technological innovation, and narrow the urban-rural income gap to support China’s agricultural green transformation.

To systematically reveal the influence mechanisms and spatial effects of industrial intellectual property (IP) reform strategies and manufacturing craftsmanship spirit on regional energy intensity, this study aims to provide theoretical support and practical pathways for emerging market economies pursuing dual goals of energy efficiency governance and manufacturing transformation. Based on a “technology–culture synergistic innovation ecology” theoretical framework, the study deepens the understanding of energy intensity governance and introduces two spatial weight matrices—the economic distance matrix and the nested economic–geographic matrix—to uncover the spatial heterogeneity of policy and cultural effects. Using panel data from 30 Chinese provinces from 2010 to 2022 (excluding Tibet, Hong Kong, Macao, and Taiwan), we construct an index of manufacturing craftsmanship spirit (CSM) and its four dimensions—excellence in detail, persistent dedication, breakthrough orientation, and innovation inheritance—via the entropy method. Empirical analysis is conducted through Spatial Difference-in-Differences (SDID) and Double Machine Learning (DML) models. The results show that: (1) Industrial IP reform strategies significantly reduce local energy intensity through improved property rights definition and technology transaction mechanisms, but may increase energy intensity in economically proximate regions due to intensified technological competition. (2) All four dimensions of craftsmanship spirit indirectly mitigate regional energy intensity via distinct pathways, with particularly strong mediating effects from persistent dedication and innovation inheritance. In contrast, breakthrough orientation shows no significant impact, possibly due to limitations from the current stage of the technology lifecycle. (3) Spatial spillover effects are heterogeneous: under the nested economic–geographic matrix, IP reform strategies reduce neighboring regions’ energy intensity through synergistic effects, while under the economic distance matrix, competitive spillovers lead to an increase in adjacent energy intensity. Based on these findings, we propose the following: deepening IP reform strategies to build a technology–culture synergistic ecosystem; enhancing regional policy coordination to avoid technology lock-in; systematically cultivating the core of craftsmanship spirit; and establishing a dynamic incentive mechanism for breakthrough orientation. These measures can jointly drive systemic improvements in regional energy efficiency.

Background The Inflation Reduction Act (IRA) did not introduce a cap on out-of-pocket (OOP) for newly approved Alzheimer’s Disease (AD) drugs, such as lecanemab which is covered under Medicare Part B. Therefore, expanding the use of conventional anti-dementia drugs is critical to addressing the growing economic burden of dementia. In this study, we aimed to evaluate the causal relationship between specific conventional anti-dementia drug use and various healthcare costs with the Double/Debiased Machine Learning (DML) approach. Methods Leveraging data from the Medicare Current Beneficiary Survey (MCBS) spanning 2015 to 2019, we utilized a nationally representative survey linked to Medicare data in this study. The presence of Alzheimer’s Disease and Related Dementias (ADRD) and anti-dementia drug use was determined through Medicare claims data. The health care costs were measured as total medical costs and categorized into Medicare costs, OOP costs, inpatient costs, and outpatient costs. Conventional anti-dementia drugs include Cholinesterase inhibitors (ChEIs) and N-methyl-D-aspartate receptor (NMDAR) antagonists. The DML techniques were employed to investigate causal relationships. Results A total of 12,764,487 weighted older adults with ADRD were included, with 34.60% of them using anti-dementia drugs. Using anti-dementia drugs could significantly reduce Medicare costs and inpatient costs by $4,804.26 and $2,842.48 on average ( P  < 0.001), while did not significantly influence total costs, OOP costs, and outpatient costs. ChEIs use could help decrease Medicare costs and inpatient costs significantly ( P  < 0.05), whereas the NMDAR antagonist (memantine) showed no statistically significant effect across all cost types. Both donepezil and rivastigmine could help significantly decrease Medicare costs and inpatient costs ( P  < 0.001). Additionally, anti-dementia drug use could significantly reduce Medicare costs and inpatient costs among non-Hispanic Whites, and significantly lower inpatient costs among non-Hispanic Blacks ( P  < 0.05). Conclusion This study revealed the causal relationship between anti-dementia drug use and Medicare costs by employing DML. ChEIs were found to be contributors to the decreased Medicare costs and inpatient costs, which could mainly be attributed to donepezil. The use of donepezil should be expanded, considering the significant benefits. Furthermore, a lower OOP cap for ADRD beneficiaries should be established under the IRA.