What are the benefits of using causal inference for ML in environmental sciences?

3 What are the benefits for environmental sciences?

Environmental sciences is a broad and interdisciplinary field that studies the interactions between natural and human systems, and their implications for sustainability, conservation, and management. To address the challenges and opportunities of environmental sciences, ML can be used for causal inference. For instance, environmental data is often complex and high-dimensional, meaning that it has many variables, interactions, and dependencies. ML can help with reducing the dimensionality, finding the relevant features, and fitting appropriate models. In addition, observational data can lead to confounding and selection bias. ML can help with adjusting for this by using techniques such as propensity score matching or inverse probability weighting. Finally, environmental data can be used to evaluate the impact of policies and interventions on the environment and society. ML can help with this by using techniques such as difference-in-differences or synthetic control.

4 What are the challenges and limitations?

Using causal inference for ML in environmental sciences is not without challenges and limitations. Ensuring validity and reliability is a major concern, as causal inference relies on assumptions that are often untestable or questionable. ML can help with testing some of these assumptions, but it cannot guarantee their validity or reliability. Additionally, ML can introduce its own sources of error or bias, such as overfitting, underfitting, or misspecification. Balancing complexity and interpretability is also important, as ML can offer powerful and flexible models that can capture complex and non-linear causal relationships, but at the cost of interpretability and transparency. Lastly, addressing ethical and social implications is essential as causal inference can have significant ethical and social implications when used to inform decisions or actions that affect the environment and society. Therefore, using causal inference for ML requires careful validation and verification, balancing the trade-off between complexity and interpretability, and addressing the ethical and social implications.

5 How to get started?

If you’re interested in using causal inference for ML in environmental sciences, there are many resources and opportunities to help you get started. To learn the basics, look into books such as Causal Inference: What If by Miguel Hernan and James Robins, courses like Causal Inference with R by Miguel Hernan, or tutorials like Causal Inference for Machine Learning by Amit Sharma. You can also explore the applications of causal inference and ML with case studies like Causal Machine Learning for Environmental Policy Evaluation by Dominik Janzing and others, the project Causal Inference for Climate Change by Judea Pearl and others, or the blog Causal Inference in Environmental Economics by Edward Rubin and others. Additionally, you can join communities such as the Causal Inference for the Brave and True group on Facebook, the Causal Inference Forum on Stack Exchange, or the Causal Inference in Environmental Sciences Workshop at NeurIPS to connect with other researchers, practitioners, and enthusiasts who are using these tools.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?