Renamed notebooks

Author: fonnesbeck

examples/statistical_rethinking_lectures/02-The_Garden_of_Forking_Data.ipynb

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+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+---
+
+(lecture_20)=
+# Horoscopes
+:::{post} Jan 7, 2024
+:tags: statistical rethinking, bayesian inference, scientific workflow
+:category: intermediate
+:author: Dustin Stansbury
+:::
+
+This notebook is part of the PyMC port of the [Statistical Rethinking 2023](https://github.com/rmcelreath/stat_rethinking_2023) lecture series by Richard McElreath.
+
+[Video - Lecture 20 - Horoscopes](https://youtu.be/qwF-st2NGTU)# [Lecture 20 - Horoscopes](https://www.youtube.com/watch?v=qwF-st2NGTU)
+
+```{code-cell} ipython3
+# Ignore warnings
+import warnings
+
+import arviz as az
+import numpy as np
+import pandas as pd
+import pymc as pm
+import statsmodels.formula.api as smf
+import utils as utils
+import xarray as xr
+
+from matplotlib import pyplot as plt
+from matplotlib import style
+from scipy import stats as stats
+
+warnings.filterwarnings("ignore")
+
+# Set matplotlib style
+STYLE = "statistical-rethinking-2023.mplstyle"
+style.use(STYLE)
+```
+
+# Horoscopes
+
+This lecture mostly outlines a set of high-level heuristics and workflows to improve the quality of scientific research. Therefore there's not a lot of implementation details in the lecture to cover. I won't go through copying the content from each slide, but I cover some highlights (mostly for my own benefit) below:
+
+## Statistics is like fortune telling
+
+- Vague facts lead to vague advice
+  - Reading tea leaves is like following common flow charts for statistical analysis
+  - There's little scientific inputs, therefore little scientific interpretation
+  - That's often the feature and the bug of fortune telling, and statistics:
+    - by providing vague interpretations (e.g. horoscope predictions) from vague inputs (e.g. birthday), they can "explain" any number of outcomes
+    - just like vague horoscopes can "explain" any number of possible future events
+- Exaggerated importance
+  - no one wants to hear evil portents in their tea leaves, just as no one wants to hear about NULL or negative statistical results
+  - there's often incentive to use statistics to find the positive result
+- It's often easier to offload subjective scientific responsibility onto objective statistical procedures
+
+## Three pillars of scientific workflow
+**1. Planning**
+   - Goal setting
+     - estimands
+   - Theory building
+     - assumptions
+     - 4 types of theory building, increasing in specificity
+       1. Heuristic (DAGs)
+          - allows us to deduce a lot from establishing causal structure
+       3. Structural
+          - moves beyond DAGs by establishing specific functional forms of causes
+       5. Dynamical models
+          - usually work over spatial/temporal grid
+          - tend to collapse large number of micro-states into macro interpretation
+       7. Agent-based
+          - focuses on individual micro states
+   - Justified sampling
+     - Which data do we use, and what's it's structure
+     - Verify with simulation
+   - Justified analysis
+     - Which golems?
+     - Can we recover estimands from simulations?
+   - Documentation
+     - How did it happen?
+     - Help others and your future self
+     - Scripting is self-documenting
+         - Comments are important
+         - Don't be clever, be explicit
+           - Avoid clever one-liners
+           - I find Python PEP useful here
+   - Sharing
+     - open source code and data formats
+     - proprietary software does not facilitate shareing, and is bad scientific ethics
+       - the irony here, is that MATLAB is so common in academic setting, particularly engineering 🙄
+     - proprietery data formats can shoot you in the foot when you (or others) can no longer open them
+  - Preregistration isn't a silver bullet
+    - Pre-allocating expectations on a bad analysis approach (e.g. causal salad) doesn't fix the bad approach
+
+**2. Working**
+
+- Research engineering
+  - Treat research more like software enginnering
+  - standardized, battle-tested procedures that make software dependable and repeatable
+    - version control (git)
+    - testing
+      - unit testing
+      - integration testing
+      - build up tests incrementally, validating each part of the workflow before proceeding to the next
+    - documentation
+    - review
+      - 👀, 👀 have at least one other person review your analysis code and docs, and provide feedback
+        - will often point out bugs, optimizations, or shortcomings in documentation
+- Look at good examples
+  - e.g. on of [McElreath's Consulting Projects](https://github.com/rmcelreath/CES_rater_2021/tree/main)
+  - [Data Carpentry](https://datacarpentry.org/)
+
+**3. Reporting**
+
+- Sharing materials
+  - by following code-based Working flow, sharing is pretty much done for you
+  - [Nice breakdown and example of Describing Methods](https://youtu.be/qwF-st2NGTU?si=3I7CMalLXv3pIQhr&t=3742)
+- Justify priors
+- Justify methods, and dealing with reviewers
+  - Common fallacy: "good scientific design doesn't require complex statistics"
+    - valid causal modeling requires complexity
+  - don't try to convince Reviewer 3 to accept your methods, write to editor
+  - move the convo from statistical to causal modeling
+- Describe data
+  - structure
+  - missing values: justify imputation if any
+- Describe results
+  - aim to report contrasts and marginal effects
+  - use densities over intervals
+  - avoid interpeting coefficients as causal effects
+- Making decisions
+  - this is often the goal (particularly in industry)
+  - embrace uncertainty
+    - uncertainty is not admission of weakness
+  - Bayesian decision theory
+    - use the posterior to simulate various policy interventions
+    - can be used to provide posteriors to costs/benefits due to those interventions
+
++++
+
+## Scientific Reform
+
+- many of the metrics for good science are counterproductive
+  - e.g. papers that are least replicated continue to have higher citation count
+  - META POINT: this result in publishing be explained using a causal modeling and colider bias
+ 
+### Collider bias in scientific publishing
+
+#### Causal model of collider bias
+
+```{code-cell} ipython3
+---
+jupyter:
+  source_hidden: true
+---
+utils.draw_causal_graph(
+    edge_list=[("newsworhiness, N", "published, P"), ("trustworthiness, T", "published, P")],
+)
+```
+
+#### Simulating data from collider causal model
+
+```{code-cell} ipython3
+np.random.seed(123)
+n_samples = 200
+
+# N and T are independent
+N = stats.norm.rvs(size=n_samples)
+T = stats.norm.rvs(size=n_samples)
+
+# Award criterion; either are large enough to threshold
+A = np.where(N + T > 2, 1, 0)
+
+for awarded in [0, 1]:
+    color = "gray" if not awarded else "C0"
+    N_A = N[A == awarded]
+    T_A = T[A == awarded]
+    utils.plot_scatter(N_A, T_A, color=color)
+
+fit_data = pd.DataFrame({"N": N_A, "T": T_A})
+cc = np.corrcoef(fit_data.T, fit_data.N)[0][1]
+awarded_model = smf.ols("T ~ N", data=fit_data).fit()
+utils.plot_line(
+    N_A, awarded_model.predict(), color="C0", label=f"Post-selection\nTrend\ncorrelation={cc:0.2}"
+)
+plt.xlabel("Newsworthiness")
+plt.ylabel("Trustworthiness")
+plt.axis("square")
+plt.legend();
+```
+
+By selecting at papers that are published based on a threshold that combines either newsworthiness--i.e. "sexy papers" that get cited a lot--or trustworthiness--i.e. boring papers that are replicable--we end up with highly-cited papers that tend to be less replicable.
+
++++ {"jp-MarkdownHeadingCollapsed": true}
+
+## Horoscopes of research
+
+- Many things that are "bad" about science (e.g. impact factor) are once well-intentioned reforms
+- Some potential fixes are available:
+  1. No stats before transparently-communicated causal model
+     - avoid causal salad
+  2. Prove your code/analysis works within the scope of your project and assumptions
+  3. Share as much as possible
+     - sometimes data is not shareable
+     - but you can create partial, anonomized, or synthetic datasets
+  4. Beware proxies for research quality (e.g. citation count, impact factor)
+
++++
+
+## Authors
+* Ported to PyMC by Dustin Stansbury (2024)
+* Based on Statistical Rethinking (2023) lectures by Richard McElreath
+
+```{code-cell} ipython3
+%load_ext watermark
+%watermark -n -u -v -iv -w -p pytensor,xarray
+```
+
+:::{include} ../page_footer.md
+:::