diff --git a/website/data/category_intros/science.md b/website/data/category_intros/science.md
index 87f70c05..1443df01 100644
--- a/website/data/category_intros/science.md
+++ b/website/data/category_intros/science.md
@@ -6,7 +6,7 @@ How to choose:
 - Optimization, integration, interpolation, and linear algebra: SciPy
 - Numeric loops that won't vectorize: Numba
 - Exact, symbolic math: SymPy
-- Regression, time series, and hypothesis tests: statsmodels
+- Regression, time series, and hypothesis tests: statsmodels; causal graphs and Bayesian networks: pgmpy
 - Biological sequences and file formats: Biopython; molecules: RDKit
 - Physical units: Pint; astronomy: Astropy; seismology: ObsPy
 - Bayesian models: PyMC
@@ -25,6 +25,8 @@ SymPy does symbolic math: expressions stay [exact, not approximate](https://docs
 
 statsmodels [estimates statistical models, runs hypothesis tests, and explores data](https://www.statsmodels.org/stable/index.html), and [most of its results are verified](https://www.statsmodels.org/stable/about.html#testing) against another statistical package. SciPy's stats docs [send regression, linear models, and time series analysis to statsmodels](https://docs.scipy.org/doc/scipy/reference/stats.html), while `scipy.stats` keeps the probability distributions and statistical tests. Write your model as an [R-style formula](https://www.statsmodels.org/stable/example_formulas.html) on a pandas DataFrame, like `smf.ols("y ~ x", data=df).fit()`, and read the fit with `summary()`.
 
+pgmpy does [causal and probabilistic reasoning with graphical models](https://pgmpy.org/). Its [quickstart](https://pgmpy.org/started/quickstart.html) follows the tasks: learn a graph's structure from data, fit the distributions for a graph you know, then query the model for probabilities or causal effects.
+
 Biopython [parses bioinformatics file formats](https://biopython.org/docs/latest/Tutorial/chapter_introduction.html) like FASTA and GenBank, queries online services like NCBI, and gives you a standard sequence class. Read sequence files with `Bio.SeqIO.parse()`, or [`Bio.SeqIO.read()` when a file holds one record](https://biopython.org/docs/latest/Tutorial/chapter_seqio.html#parsing-or-reading-sequences). When you query NCBI through `Bio.Entrez`, [pass your email](https://biopython.org/docs/latest/Tutorial/chapter_entrez.html#entrez-guidelines) so NCBI can contact you if there's a problem. RDKit is a [cheminformatics toolkit with its core in C++](https://www.rdkit.org/docs/Overview.html), for 2D and 3D molecular operations and descriptors for machine learning. To compare molecules, [create a fingerprint generator](https://www.rdkit.org/docs/GettingStartedInPython.html#fingerprinting-and-molecular-similarity) for the fingerprint type you want, the docs' most consistent way to get fingerprints.
 
 Pint handles [physical quantities](https://pint.readthedocs.io/en/stable/getting/overview.html): a value times a unit, in any numeric type. In a package, [create one `UnitRegistry` in a single place](https://pint.readthedocs.io/en/stable/getting/pint-in-your-projects.html#having-a-shared-registry) and import it everywhere, since quantities from different registries don't mix. Astropy is [a common core package for astronomy](https://www.astropy.org/), with affiliated packages around it. [Import the subpackage you need](https://docs.astropy.org/en/stable/importing_astropy.html#importing-astropy-and-sub-packages), like `from astropy import units as u`, and never import with `*`. ObsPy is [a framework for processing seismological data](https://docs.obspy.org/): `read()` loads [SAC, MiniSEED, and other formats into a Stream](https://docs.obspy.org/tutorial/code_snippets/reading_seismograms.html) of Traces, and its [FDSN client](https://docs.obspy.org/packages/obspy.clients.fdsn.html#basic-fdsn-client-usage) fetches data from data centers.