Package: hcm-causal
py-tetradThis package shows how to make arbitrary code inTetraddirectly available in Python viaJPypeas part of a Python workflow. We do this by givingreusable examplesof how it can be done, along withAPI Javadoc documentationto allow further exploration of the entire Tetrad codebase.It also gives some simple tools that can be used in both Python and R to hide the JPype facilities for those who don't want to (or can't, in the case of R) deal directly with the Tetrad codebase.Part of our code uses thecausal-learn Python packageinpy-whyto show how it can be integrated.You can also integrate Tetrad code into Python by making os.system (..) calls toCausal Command; here aresome examplesof how to do it.Please bear with us as we add and refine example modules and keep our code up to date. Please submit any problems or suggestions to ourIssue Tracker, so that we can resolve them. In some cases it may not be obvious how to call a Tetrad class or method from Python. Please point out any difficulties you have, so that we can make it more intuitive to use for the next version.We maintain acurrent version of the Tetrad launch jar, which is either thecurrent published versionor else the current published version with someadjustments. The example code will work with this current jar. Feel free to use any version of Tetrad though. All artifacts for Tetrad for the last several releases are onMaven Central, with their corresponding API Javadocs, along wth signatures to verify authenticity.We addeda method to use Tetrad algorithms in R via py-tetrad. This is work in progress.InstallIt is necessary to install a JDK. For this, see our Wiki article,Setting up Java for Tetrad.If JAVA_HOME is not already set to the correct location of your Java installation above, you'll need toset JAVA_HOMEto the path of the Java installation you want to use for py-tetrad.Make sure you are using the latest Python--at least 3.5--as required by JPype; if not,update it.We use causal-learn. For installation instructions, see theDocs for the causal-learn package.We use the JPype package to interface Python with Java. For installation instructions, see theDocs for the JPype box.Finally, you will need to clone this GitHub repository, so if you don't have Git installed, google and install that for your machine type.Then (for instance, on a Mac) in a terminal window, cd to a directory where you want the cloned project to appear and type the following--again, as above, make sure JAVA_HOME is set correctly to your java path:git clone https://github.com/cmu-phil/py-tetrad/
cd py-tetrad/pytetrad
python run_continuous.pyIf everything is set up right, the last command should cause this example module to run various algorithms in Tetrad and print out result graphs. Feel free to explore other example modules in that directory.Feel free to use your favorite method for editing and running modules.CitationPlease cite as:Bryan Andrews and Joseph Ramsey.https://github.com/cmu-phil/py-tetrad, 2023.DAGs with NO TEARS :no_entry_sign::droplet:[Update 12/8/22]Interested in faster and more accurate structure learning? See our newDAGMAlibrary fromNeurIPS 2022.This is an implementation of the following papers:[1] Zheng, X., Aragam, B., Ravikumar, P., & Xing, E. P. (2018).DAGs with NO TEARS: Continuous optimization for structure learning(NeurIPS 2018, Spotlight).[2] Zheng, X., Dan, C., Aragam, B., Ravikumar, P., & Xing, E. P. (2020).Learning
sparse nonparametric DAGs(AISTATS 2020, to appear).If you find this code useful, please consider citing:@inproceedings{zheng2018dags,
    author = {Zheng, Xun and Aragam, Bryon and Ravikumar, Pradeep and Xing, Eric P.},
    booktitle = {Advances in Neural Information Processing Systems},
    title = {{DAGs with NO TEARS: Continuous Optimization for Structure Learning}},
    year = {2018}
}@inproceedings{zheng2020learning,
    author = {Zheng, Xun and Dan, Chen and Aragam, Bryon and Ravikumar, Pradeep and Xing, Eric P.},
    booktitle = {International Conference on Artificial Intelligence and Statistics},
    title = {{Learning sparse nonparametric DAGs}},
    year = {2020}
}tl;dr Structure learning in <60 linesCheck outlinear.pyfor a complete, end-to-end implementation of the NOTEARS algorithm in fewer than60 lines.This includes L2, Logistic, and Poisson loss functions with L1 penalty.IntroductionA directed acyclic graphical model (aka Bayesian network) withdnodes defines a
distribution of random vector of sized.
We are interested in the Bayesian Network Structure Learning (BNSL) problem:
givennsamples from such distribution, how to estimate the graphG?A major challenge of BNSL is enforcing the directed acyclic graph (DAG)
constraint, which iscombinatorial.
While existing approaches rely on local heuristics,
we introduce a fundamentally different strategy: we formulate it as a purelycontinuousoptimization problem over real matrices that avoids this
combinatorial constraint entirely.
In other words,wherehis asmoothfunction whose level set exactly characterizes the
space of DAGs.RequirementsPython 3.6+numpyscipypython-igraph: Installigraph C coreandpkg-configfirst.torch: Optional, only used for nonlinear model.Contents (New version)linear.py- the 60-line implementation of NOTEARS with l1 regularization for various lossesnonlinear.py- nonlinear NOTEARS using MLP or basis expansionlocally_connected.py- special layer structure used for MLPlbfgsb_scipy.py- wrapper for scipy's LBFGS-Butils.py- graph simulation, data simulation, and accuracy evaluationRunning a simple demoThe simplest way to try out NOTEARS is to run a simple example:$gitclonehttps://github.com/xunzheng/notears.git
$cdnotears/
$pythonnotears/linear.pyThis runs the l1-regularized NOTEARS on a randomly generated 20-node Erdos-Renyi graph with 100 samples.
Within a few seconds, you should see output like this:{'fdr': 0.0, 'tpr': 1.0, 'fpr': 0.0, 'shd': 0, 'nnz': 20}The data, ground truth graph, and the estimate will be stored inX.csv,W_true.csv, andW_est.csv.Running as a commandAlternatively, if you have a CSV data fileX.csv, you can install the package and run the algorithm as a command:$pipinstallgit+git://github.com/xunzheng/notears
$notears_linearX.csvThe output graph will be stored inW_est.csv.Examples: Erdos-Renyi graphGround truth:d = 20nodes,2d = 40expected edges.Estimate withn = 1000samples:lambda = 0,lambda = 0.1, andFGS(baseline).Bothlambda = 0andlambda = 0.1are close to the ground truth graph
whennis large.Estimate withn = 20samples:lambda = 0,lambda = 0.1, andFGS(baseline).Whennis small,lambda = 0perform worse whilelambda = 0.1remains accurate, showing the advantage of L1-regularization.Examples: Scale-free graphGround truth:d = 20nodes,4d = 80expected edges.The degree distribution is significantly different from the Erdos-Renyi graph.
One nice property of our method is that it is agnostic about the
graph structure.Estimate withn = 1000samples:lambda = 0,lambda = 0.1, andFGS(baseline).The observation is similar to Erdos-Renyi graph:
bothlambda = 0andlambda = 0.1accurately estimates the ground truth
whennis large.Estimate withn = 20samples:lambda = 0,lambda = 0.1, andFGS(baseline).Similarly,lambda = 0suffers from smallnwhilelambda = 0.1remains accurate, showing the advantage of L1-regularization.Other implementationsPython:https://github.com/jmoss20/notearsTensorflow with Python:https://github.com/ignavier/notears-tensorflowDAGMA: Faster and more accurate structure learning with a log-det constraintThis is an implementation of the following paper:[1] Bello K., Aragam B., Ravikumar P. (2022).DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization.NeurIPS'22.If you find this code useful, please consider citing:@inproceedings{bello2022dagma,
    author = {Bello, Kevin and Aragam, Bryon and Ravikumar, Pradeep},
    booktitle = {Advances in Neural Information Processing Systems},
    title = {{DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization}},
    year = {2022}
}Table of ContentSummaryThe log-det acyclicity characterizationA path-following approachRequirementsContentsRunning DAGMAAcknowledgmentsSummaryWe propose a new acyclicity characterization of DAGs via a log-det function for learning DAGs from observational data. Similar to previously proposed acyclicity functions (e.g.NOTEARS), our characterization is also exact and differentiable. However, when compared to existing characterizations, our log-det function: (1) Is better at detecting large cycles; (2) Has better-behaved gradients; and (3) Its runtime is in practice about an order of magnitude faster. These advantages of our log-det formulation, together with a path-following scheme, lead to significant improvements in structure accuracy (e.g. SHD).The log-det acyclicity characterizationLet $W \in \mathbb{R}^{d\times d}$ be a weighted adjacency matrix  of a graph of $d$ nodes, the log-det function takes the following form:$$h^{s}(W) = -\log \det (sI-W\circ W) + d \log s,$$where $I$ is the identity matrix, $s$ is a given scalar (e.g., 1), and $\circ$ denotes the element-wise Hadamard product. Of particular interest, we have that $h(W) = 0$ if and only if $W$ represents a DAG, and when the domain of $h$ is the set of M-matrices then $h$ is well-defined and non-negative. For more properties of $h(W)$ (e.g., being an invex function), $\nabla h(W)$, and $\nabla^2 h(W)$, we invite you to look at [1].A path-following approachGiven the exact differentiable characterization of a DAG, we are interested in solving the following optimization problem:\begin{array}{cl}
\min _{W \in \mathbb{R}^{d \times d}} & Q(W;\mathbf{X}) \\
\text { subject to } & h^{s}(W) = 0,
\end{array}where $Q$ is a given score function (e.g., square loss) that depends on $W$ and the dataset $\mathbf{X}$. To solve the above constrained problem, we propose a path-following approach where we solve a few of the following unconstrained problems:\hat{W}^{(t+1)} = \arg\min_{W}\; \mu^{(t)} Q(W;\mathbf{X}) + h(W),where $\mu^{(t)} \to 0$ as $t$ increases. Leveraging the properties of $h$, we show that, at the limit, the solution is a DAG. The trick to make this work is touse the previous solution as a starting point when solving the current unconstrained problem, as usually done in interior-point algorithms. Finally, we use a simple accelerated gradient descent method to solve each unconstrained problem.Let us give an illustration of how DAGMA works in a two-node graph (see Figure 1 in [1] for more details). Here $w_1$ (the x-axis) represents the edge weight from node 1 to node 2; while $w_2$ (y-axis) represents the edge weight from node 2 to node 1. Moreover, in this example, the ground-truth DAG corresponds to $w_1 = 1.2$ and $w_2 = 0$.Below we have 4 plots, where each illustrates the solution to an unconstrained problem for different values of $\mu$. In the top-left plot, we have $\mu=1$, and we solve the unconstrained problem starting at the empty graph (i.e., $w_1 = w_2 = 0$), which corresponds to the red point, and after running gradient descent, we arrive at the cyan point (i.e., $w_1 = 1.06, w_2 = 0.24$). Then, for the next unconstrained problem in the top-right plot, we have $\mu = 0.1$, and we initialize gradient descent at the previous solution, i.e., $w_1 = 1.06, w_2 = 0.24$, and arrive at the cyan point $w_1 = 1.16, w_2 = 0.04$. Similarly, DAGMA solves for $\mu=0.01$ and $\mu=0.001$, and we can observe how the solution at the final iteration (bottom-right plot) is close to the ground-truth DAG  $w_1 = 1.2, w_2 = 0$.RequirementsPython 3.6+numpyscipypython-igraphtorch: Only used for nonlinear models.Contentsdagma_linear.py- implementation of DAGMA for linear models with l1 regularization (supports L2 and Logistic losses).dagma_nonlinear.py- implementation of DAGMA for nonlinear models using MLPlocally_connected.py- special layer structure used for MLPutils.py- graph simulation, data simulation, and accuracy evaluationRunning DAGMAUserequirements.txtto install the dependencies (recommended to use virtualenv or conda).
The simplest way to try out DAGMA is to run a simple example:$gitclonehttps://github.com/kevinsbello/dagma.git
$cddagma/
$pip3install-rrequirements.txt
$python3dagma_linear.pyThe above runs the L1-regularized DAGMA on a randomly generated 20-node Erdos-Renyi graph with 500 samples.
Within a few seconds, you should see an output like this:{'fdr': 0.0, 'tpr': 1.0, 'fpr': 0.0, 'shd': 0, 'nnz': 20}The data, ground truth graph, and the estimate will be stored inX.csv,W_true.csv, andW_est.csv.AcknowledgmentsWe thank the authors of theNOTEARS repofor making their code available. Part of our code is based on their implementation, specially theutils.pyfile and some code from their implementation of nonlinear models.pysynthdid : Synthetic difference in differences for PythonWhat is Synthetic difference in differences:original paper:Arkhangelsky, Dmitry, et al. Synthetic difference in differences. No. w25532. National Bureau of Economic Research, 2019.https://www.nber.org/papers/w25532R pkg:https://github.com/synth-inference/synthdidBlog:https://medium.com/@masa_asami/causal-inference-using-synthetic-difference-in-differences-with-python-5758e5a76909Installation:$ pip install git+https://github.com/MasaAsami/pysynthdidThis package is still under development. I plan to register withpypiafter the following specifications are met.Refactoring and better documentationCompletion of the TEST codeHow to use:Here's a simple example :setupfromsynthdid.modelimportSynthDIDfromsynthdid.sample_dataimportfetch_CaliforniaSmokingdf=fetch_CaliforniaSmoking()PRE_TEREM=[1970,1988]POST_TEREM=[1989,2000]TREATMENT=["California"]estimation & plotsdid=SynthDID(df,PRE_TEREM,POST_TEREM,TREATMENT)sdid.fit(zeta_type="base")sdid.plot(model="sdid")Details of each method will be created later.See the jupyternotebookfor basic usageReproductionExperiment_CaliforniaSmoking.ipynbThis is a reproduction experiment note of the original paper, using a famous dataset (CaliforniaSmoking).OtherOmegaEstimationMethods.ipynbThis note is a different take on the estimation method for parameteromega(&zeta). As a result, it confirms the robustness of the estimation method in the original paper.ScaleTesting_of_DonorPools.ipynbIn this note, we will check how the estimation results change with changes in the scale of the donor pool features.Adding donor pools with extremely different scales (e.g., 10x) can have a significant impact (bias) on the estimates.If different scales are mixed, as is usually the case in traditional regression, preprocessing such as logarithmic transformation is likely to be necessaryDiscussions and PR:This module is still under development.If you have any questions or comments, please feel free to use issues.python setup.py sdist bdist_wheel
twine upload dist/*