We need to talk about LLM Themes. Somewhere along the way, the standard advice in data pipelines became treating GPT-extracted labels as fixed facts, and it’s wrecking the integrity of a lot of causal models. I’ve seen teams join LLM-extracted themes from call transcripts straight onto customer tables, fill the NULLs with zeros, and ship a regression model that claims to “prove” a product roadmap.
The problem is that these generated variables are not observations. They are model outputs conditioned on behavior, and more precisely on a customer actually doing something that left a textual trace. Treat them like a normal database column and you’re smuggling in a data-generating process your downstream analysis never modeled.
The NULL fill problem with LLM themes
When an analyst joins transcripts to a customer table, the customers who never called get a NULL. The usual “hack” is to fill that NULL with zero or a label like “no issue mentioned.” So in one line of Python, the pipeline turns “did not generate text” into “did not experience billing frustration.”
That’s plain selection bias. A theme exists because a customer complained or posted, and that behavior is rarely independent of the outcome. Zero-filling quietly redefines your population during preprocessing without anyone saying so. I’ve written more on how this hits business logic in my post on causal inference analysis and measuring real impact.
Four ways your pipeline lies to you
I’ve fought with these pipelines for years, and four problems usually break the logic at the same time:
- Selection: Whether LLM Themes exist at all depends on treatment. If your retention offer changes how often people call, your selection mechanism isn’t stable anymore.
- Timing: If the text was generated after the treatment, it’s a mediator or an outcome, not a confounder. Using it as a control is textbook post-treatment bias.
- Measurement: Label noise isn’t independent of treatment. If a treatment changes how customers talk, the classifier’s accuracy differs between arms.
- Role: The DAG (Directed Acyclic Graph) decides what role a variable plays, not the column name.
The naive regression trap
Here’s what that looks like in code. Picture a synthetic setup where we target price-sensitive customers with a retention offer. Because churn affects how likely someone is to call, conditioning on a “bill shock” theme flips the sign of your coefficients.
import numpy as np
import pandas as pd
import statsmodels.api as sm
# Simulated biased pipeline
rng = np.random.default_rng(7)
n = 20000
price_sens = rng.normal(0, 1, n)
offer = rng.binomial(1, 1 / (1 + np.exp(-(0.8 * price_sens))))
churn = rng.binomial(1, 1 / (1 + np.exp(-(-1.0 + 1.2 * price_sens - 0.5 * offer))))
called = rng.binomial(1, 1 / (1 + np.exp(-(-1.5 + 0.7 * price_sens + 0.9 * churn))))
# The "LLM" Extraction
theme_prob = 1 / (1 + np.exp(-(-0.5 + 0.8 * price_sens)))
bill_shock = np.where(called == 1, rng.binomial(1, theme_prob), 0)
df = pd.DataFrame({"churn": churn, "offer": offer, "bill_shock": bill_shock})
X = sm.add_constant(df[["offer", "bill_shock"]])
naive = sm.Logit(df["churn"], X).fit(disp=0)
print(naive.params)In this setup the offer actually reduces churn. But because the analyst used LLM Themes as a bad control, the model will likely show the offer increasing churn. The product team sees that, panics, and kills a campaign that was working.
Differential measurement error in LLM themes
LLM outputs also don’t look noisy. They look like latent constructs. A label like “Trust Erosion” reads like a medical diagnosis, but it’s a noisy proxy. If your retention offer softens how customers talk, the model may flag “frustration” language less often while the frustration itself stays exactly where it was. That kind of differential error makes your results impossible to interpret.
Researchers like Egami et al. and Mozer et al. have built workflows for this, but they only work once you admit the label is a measurement. Most WordPress and data teams skip that step in the rush to use AI data analysis as a shortcut.
If this LLM Themes stuff is eating your dev hours, let me handle it. I’ve been working with WordPress since the 4.x days.
Diagnostic checklist for generated variables
Before you run that regression, you owe the project five answers:
- Role: Is it a confounder or a mediator? (The DAG decides.)
- Timing: Was the text written before or after treatment?
- Selection: What are you assuming about the NULLs?
- Reliability: Does the treatment change how customers express the construct?
- Stress test: Does the headline coefficient hold up if you drop the theme entirely?
If you can’t answer these, you’re doing descriptive work with causal language bolted on. Using an LLM didn’t make the assumptions go away. It moved them upstream into your preprocessing.
