Added related work and ongoing/future work sections

Author: jmafoster1

paper/paper.bib

@@ -1,3 +1,14 @@
+@article{blobaum2024dowhy,
+  author = {Patrick Bl{{\"o}}baum and Peter G{{\"o}}tz and Kailash Budhathoki and Atalanti A. Mastakouri and Dominik Janzing},
+  journal = {Journal of Machine Learning Research},
+  number = {147},
+  pages = {1--7},
+  title = {DoWhy-GCM: An Extension of DoWhy for Causal Inference in Graphical Causal Models},
+  url = {http://jmlr.org/papers/v25/22-1258.html},
+  volume = {25},
+  year = {2024}
+}
+
 @techreport{chen1998metamorphic,
   author = {Chen, Tsong Y. and Cheung, Shing C. and Yiu, Shiu Ming},
   institution = { The Hong Kong University of Science and Technology},
@@ -59,3 +70,35 @@ @inproceedings{guderlei2007smt
   volume = {},
   year = {2007}
 }
+
+@misc{sharma2020dowhy,
+  archiveprefix = {arXiv},
+  author = {Amit Sharma and Emre Kiciman},
+  eprint = {2011.04216},
+  primaryclass = {stat.ME},
+  title = {DoWhy: An End-to-End Library for Causal Inference},
+  url = {https://arxiv.org/abs/2011.04216},
+  year = {2020}
+}
+
+@article{somers2024configuration,
+  doi = {10.2139/ssrn.4732706},
+  author = {Somers, Richard and Walkinshaw, Neil and Hierons, Robert and Elliott, Jackie and Iqbal, Ahmed and Walkinshaw, Emma},
+  publisher = {Elsevier BV},
+  title = {Configuration Testing of an Artificial Pancreas System Using a Digital Twin},
+  url = {http://dx.doi.org/10.2139/ssrn.4732706},
+  year = {2024}
+}
+
+@article{textor2017dagitty,
+  doi = {10.1093/ije/dyw341},
+  issn = {1464-3685},
+  author = {Textor, Johannes and van der Zander, Benito and Gilthorpe, Mark S. and Liśkiewicz, Maciej and Ellison, George T.H.},
+  journal = {International Journal of Epidemiology},
+  month = {jan},
+  pages = {dyw341},
+  publisher = {Oxford University Press (OUP)},
+  title = {Robust causal inference using directed acyclic graphs: the R package ‘dagitty’},
+  url = {http://dx.doi.org/10.1093/ije/dyw341},
+  year = {2017}
+}

paper/paper.md

@@ -85,6 +85,18 @@ Many scientific models involve iterating several interacting processes over time
 These processes often feed into each other, and can create feedback cycles.
 Traditional Causal Inference cannot handle this, however the Causal Testing Framework uses another advanced Causal Inference technique, g-methods, to enable the estimation of causal effects even when there are feedback cycles between variables.
 
+# Related Work
+The Dagitty tool [@textor2017dagitty] is a browser-based environment for creating, editing, and analysing causal graphs.
+There is an R package for local use, but the tool does not aim to facilitate causal inference.
+For this, the doWhy [@sharma2020dowhy; @blobaum2024dowhy] is a python package which can be used to estimate causal effects from data.
+However, the package is intended for general causal inference.
+It does not explicitly support causal testing, nor does it support temporal feedback loops.
+
+# Ongoing and Future Research
+The Causal Testing Framework is the subject of several publications [@clark2023metamorphic; @clark2023testing; @foster2024adequacy; @somers2024configuration].
+We are also in the process of preparing scientific publications concerning how the Causal Testing Framework handles missing variables and feedback over time.
+Furthermore, we are working to develop a plug-in for the [DAFNI framework](https://www.dafni.ac.uk/) to enable national-scale infrastructure models to be easily tested.
+
 # Acknowledgements
 This work was supported by the EPSRC CITCoM grant EP/T030526/1.