CausalEGM is a general causal inference framework for estimating causal effects by encoding generative modeling, which can be applied in both discrete and continuous treatment settings. A description of the methods is given in Liu (2022) <arXiv:2212.05925>.

Generate causally-simulated data to serve as ground truth for evaluating methods in causal discovery and effect estimation. The package provides tools to assist in defining functions based on specified edges, and conversely, defining edges based on functions. It enables the generation of data according to these predefined functions and causal structures. This is particularly useful for researchers in fields such as artificial intelligence, statistics, biology, medicine, epidemiology, economics, and social sciences, who are developing a general or a domain-specific methods to discover causal structures and estimate causal effects. Data simulation adheres to principles of structural causal modeling. Detailed methodologies and examples are documented in our vignette, available at <https://htmlpreview.github.io/?https://github.com/herdiantrisufriyana/rcausim/blob/master/doc/causal_simulation_exemplar.html>.

Provides implementations of a classifier based on the
    "Classification Based on Associations" (CBA). It can be used for building
    classification models from association rules. Rules are pruned in the order of
    precedence given by the sort criteria and a default rule is added. The final
    classifier labels provided instances. CBA was originally proposed by Liu,
    B. Hsu, W. and Ma, Y. Integrating Classification and Association Rule
    Mining. Proceedings KDD-98, New York, 27-31 August. AAAI. pp80-86 (1998, ISBN:1-57735-070-7).

Tools for large, sparse optimal matching of treated units
	and control units in observational studies.  Provisions are
	made for refined covariate balance constraints, which include
	fine and near-fine balance as special cases.  Matches are 
	optimal in the sense that they are computed as solutions to
	network optimization problems rather than greedy algorithms.
	See Pimentel, et al.(2015) <doi:10.1080/01621459.2014.997879> 
	and Pimentel (2016), Obs. Studies 2(1):4-23. The rrelaxiv 
	package, which provides an alternative solver for
	the underlying network flow problems, carries an
	academic license and is not available on CRAN, but
	may be downloaded from Github at 
	<https://github.com/josherrickson/rrelaxiv/>.