We need to talk about LLM Themes. For some reason, the standard advice in modern data pipelines has become treating GPT-extracted labels as immutable facts. This is a dangerous trend that’s killing the integrity of our causal models. I’ve seen teams join LLM-extracted themes from call transcripts directly to 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. Specifically, they are conditional on a customer actually doing something that left a textual trace. Consequently, if you treat them like a standard database column, you’re smuggling in a data-generating process your downstream analysis never modeled.
The Fallacy of the NULL Move in LLM Themes
When an analyst joins transcripts to a customer table, the customers who didn’t call get a NULL. The common “hack” is to fill that NULL with zero or a label like “no issue mentioned.” Therefore, in one line of Python, the pipeline converts “did not generate text” into “did not experience billing frustration.”
This is a classic selection bias. A theme exists because a customer complained or posted. That behavior is rarely orthogonal to the outcome. By zero-filling, you are redefined your population in preprocessing without stating it. For a deeper look at how this impacts business logic, check out my thoughts on causal inference analysis and measuring real impact.
Four Ways Your Pipeline Lies to You
I’ve wrestled with these pipelines for years, and four issues tend to break the logic simultaneously:
- Selection: The existence of LLM Themes is treatment-dependent. If your retention offer changes calling rates, your selection mechanism is no longer stable.
- Timing: If the text was generated after the treatment, it’s a mediator or an outcome, not a confounder. Treating it as a control is a classic post-treatment bias.
- Measurement: Label noise is not orthogonal. If a treatment changes how customers talk, the classifier’s accuracy differs between arms.
- Role: The DAG (Directed Acyclic Graph) decides the role of the variable, not the column name.
The Naive Regression Trap
Let’s look at a “war story” in code. Imagine a synthetic setup where we target price-sensitive customers with a retention offer. Because churn affects the likelihood of calling, conditioning on a “bill shock” theme induces a massive sign flip in 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 scenario, the offer actually reduces churn. However, because the analyst used LLM Themes as a bad control, the model will likely show the offer increasing churn. The product team sees this, panics, and kills a successful campaign.
Differential Measurement Error in LLM Themes
Furthermore, LLM outputs don’t look noisy; they look like latent constructs. A label like “Trust Erosion” reads like a medical diagnosis. But it’s just a noisy proxy. If your retention offer softens customer sentiment, it might reduce the rate at which the model flags “frustration” language without actually reducing the underlying frustration. This differential error makes your results uninterpretable.
Researchers like Egami et al. and Mozer et al. have developed workflows for this, but they require you to admit the label is a measurement first. This is a step most WordPress and data teams skip in the rush to use AI data analysis as a shortcut.
Look, if this LLM Themes stuff is eating up your dev hours, let me handle it. I’ve been wrestling with WordPress since the 4.x days.
Diagnostic Checklist for Generated Variables
Specifically, 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 pre-treatment or post-treatment?
- Selection: What assumption are you making about the NULLs?
- Reliability: Does the treatment change how customers express the construct?
- Stress Test: Does the headline coefficient survive if you drop the theme entirely?
If you can’t answer these, you’re just doing descriptive work with causal language attached. The assumptions didn’t disappear just because you used an LLM; they just moved upstream into your preprocessing pipeline.
