Notebook template

example

marginalization, conditioning

khanaman example https://comosoco24.daeh.info/sections/conditioning 3blue1brown

marginalization

CONDITIONING AND MARGINALIXATION ( https://comosoco24.daeh.info/sections/conditioning ) ( /Users/dae/coding/-sandbox/memo-sandbox/code/webppl_ports/conditional_dependence.py )

https://www.youtube.com/watch?v=mBEA7PKDmiY&list=PLDcUM9US4XdPz-KxHM4XHt7uUVGWWVSus&index=6 https://comosoco24.daeh.info/lectures/lec7t https://comosoco24.daeh.info/sections/dependence

spurious correlations http://www.tylervigen.com/spurious-correlations

how could we try to test for causal? => intervention

Causal relations are local, modular, and directed. They are modular in the sense that any two arbitrary events in the world are most likely causally unrelated, or independent. If they are related, or dependent, the relation is only very weak and liable to be ignored in our mental models. Causal structure is local in the sense that many events that are related are not related directly: They are connected only through causal chains of several steps, a series of intermediate and more local dependencies. And the basic dependencies are directed: when we say that A causes B, it means something different than saying that B causes A. The causal influence flows only one way along a causal relation—we expect that manipulating the cause will change the effect, but not vice versa—but information can flow both ways—learning about either event will give us information about the other. https://comosoco24.daeh.info/sections/dependence

do calculus

mcelreath ch 6 marrage example backdoor criterion, adjustment sets https://www.youtube.com/watch?v=U1S8Rq8IcrY statistical and causal dependence stratification

Let’s examine this notion of “causal dependence” a little more carefully. What does it mean to believe that A depends causally on B? Viewing cognition through the lens of probabilistic programs, the most basic notions of causal dependence are in terms of the structure of the program and the flow of evaluation (or “control”) in its execution. We say that expression A causally depends on expression B if it is necessary to evaluate B A in order to evaluate. (More precisely, expression A depends on expression if it is ever necessary to evaluate B in order to evaluate A.) For instance, in this program A depends on B but not on C (the final expression depends on both A and C):

var C = flip()
var B = flip()
var A = B ? flip(0.1) : flip(0.4)
A || C

Note that causal dependence order is weaker than a notion of ordering in time—one expression might happen to be evaluated before another in time (for instance C before A), but without the second expression requiring the first. (This notion of causal dependence is related to the notion of flow dependence in the programming language literature.)

// https://comosoco24.daeh.info/sections/dependence
var marg = Infer({method: 'enumerate'}, function() {
  var smokes = flip(0.2)
  var lungDisease = (smokes && flip(0.1)) || flip(0.001)
  var cold = flip(0.02)
  var cough = (cold && flip(0.5)) || (lungDisease && flip(0.5)) || flip(0.001)
  var fever = (cold && flip(0.3)) || flip(0.01)
  var chestPain = (lungDisease && flip(0.2)) || flip(0.01)
  var shortnessOfBreath = (lungDisease && flip(0.2)) || flip(0.01)

  condition(cough)
  return {cold: cold, lungDisease: lungDisease}
})

traditions of modeling some concerned with correlation, prediction CNN, LLM decoders / classifiers other with ontological structure in science, what we care about is ontological structure

medical diagnosis - intervention on symptom vs disease

counterfactuals and the do operator

(((random variables))) (((marginals))) (((conditioning)))

estimand - hedghog cake estimator - recipe

https://youtu.be/mBEA7PKDmiY?si=1c4rAvjXrWYQ4osK&t=2854 collider the causes are not in the data

@memo
def f[y: X]():
    alice: thinks[
        bob: chooses(x in X, wpp=1),
        bob: chooses(y in X, wpp=abs(x - y) < 0.5)
    ]

    alice: snapshots_self_as(pre_alice)
    alice: observes [bob.y] is y
    alice: snapshots_self_as(post_alice)

    return alice[ imagine[
        post_alice[ -H[bob.x] ] - pre_alice[ -H[bob.x] ]
    ] ]
%reset -f
import sys
import platform
import importlib.metadata

print("Python:", sys.version)
print("Platform:", platform.system(), platform.release())
print("Processor:", platform.processor())
print("Machine:", platform.machine())

print("\nPackages:")
for name, version in sorted(
    ((dist.metadata["Name"], dist.version) for dist in importlib.metadata.distributions()),
    key=lambda x: x[0].lower()  # Sort case-insensitively
):
    print(f"{name}=={version}")
Python: 3.13.2 (main, Feb  5 2025, 18:58:04) [Clang 19.1.6 ]
Platform: Darwin 23.6.0
Processor: arm
Machine: arm64

Packages:
annotated-types==0.7.0
anyio==4.9.0
appnope==0.1.4
argon2-cffi==23.1.0
argon2-cffi-bindings==21.2.0
arrow==1.3.0
astroid==3.3.9
asttokens==3.0.0
async-lru==2.0.5
attrs==25.3.0
babel==2.17.0
beautifulsoup4==4.13.3
bleach==6.2.0
certifi==2025.1.31
cffi==1.17.1
cfgv==3.4.0
charset-normalizer==3.4.1
click==8.1.8
comm==0.2.2
contourpy==1.3.1
cycler==0.12.1
debugpy==1.8.13
decorator==5.2.1
defusedxml==0.7.1
dill==0.3.9
distlib==0.3.9
executing==2.2.0
fastjsonschema==2.21.1
filelock==3.18.0
fonttools==4.56.0
fqdn==1.5.1
h11==0.14.0
httpcore==1.0.7
httpx==0.28.1
identify==2.6.9
idna==3.10
importlib_metadata==8.6.1
ipykernel==6.29.5
ipython==9.0.2
ipython_pygments_lexers==1.1.1
ipywidgets==8.1.5
isoduration==20.11.0
isort==6.0.1
jax==0.5.3
jaxlib==0.5.3
jedi==0.19.2
Jinja2==3.1.6
joblib==1.4.2
json5==0.10.0
jsonpointer==3.0.0
jsonschema==4.23.0
jsonschema-specifications==2024.10.1
jupyter-cache==1.0.1
jupyter-events==0.12.0
jupyter-lsp==2.2.5
jupyter_client==8.6.3
jupyter_core==5.7.2
jupyter_server==2.15.0
jupyter_server_terminals==0.5.3
jupyterlab==4.3.6
jupyterlab_pygments==0.3.0
jupyterlab_server==2.27.3
jupyterlab_widgets==3.0.13
kiwisolver==1.4.8
MarkupSafe==3.0.2
matplotlib==3.10.1
matplotlib-inline==0.1.7
mccabe==0.7.0
memo-lang==1.1.2
mistune==3.1.3
ml_dtypes==0.5.1
nbclient==0.10.2
nbconvert==7.16.6
nbformat==5.10.4
nest-asyncio==1.6.0
networkx==3.4.2
nodeenv==1.9.1
notebook_shim==0.2.4
numpy==2.2.4
opt_einsum==3.4.0
optype==0.9.2
overrides==7.7.0
packaging==24.2
pandas==2.2.3
pandas-stubs==2.2.3.250308
pandocfilters==1.5.1
parso==0.8.4
pexpect==4.9.0
pillow==11.1.0
platformdirs==4.3.7
plotly==5.24.1
pre_commit==4.2.0
prometheus_client==0.21.1
prompt_toolkit==3.0.50
psutil==7.0.0
ptyprocess==0.7.0
pure_eval==0.2.3
pycparser==2.22
pydantic==2.10.6
pydantic_core==2.27.2
Pygments==2.19.1
pygraphviz==1.14
pylint==3.3.6
pyparsing==3.2.3
python-dateutil==2.9.0.post0
python-dotenv==1.1.0
python-json-logger==3.3.0
pytz==2025.2
PyYAML==6.0.2
pyzmq==26.3.0
referencing==0.36.2
requests==2.32.3
rfc3339-validator==0.1.4
rfc3986-validator==0.1.1
rpds-py==0.23.1
ruff==0.11.2
scikit-learn==1.6.1
scipy==1.15.2
scipy-stubs==1.15.2.1
seaborn==0.13.2
Send2Trash==1.8.3
setuptools==78.1.0
six==1.17.0
sniffio==1.3.1
soupsieve==2.6
SQLAlchemy==2.0.39
stack-data==0.6.3
tabulate==0.9.0
tenacity==9.0.0
terminado==0.18.1
threadpoolctl==3.6.0
tinycss2==1.4.0
toml==0.10.2
tomlkit==0.13.2
tornado==6.4.2
tqdm==4.67.1
traitlets==5.14.3
types-python-dateutil==2.9.0.20241206
types-pytz==2025.1.0.20250318
typing_extensions==4.12.2
tzdata==2025.2
uri-template==1.3.0
urllib3==2.3.0
virtualenv==20.29.3
wcwidth==0.2.13
webcolors==24.11.1
webencodings==0.5.1
websocket-client==1.8.0
widgetsnbextension==4.0.13
xarray==2025.3.0
zipp==3.21.0