Causal inference methods allow you to quantify impact when randomized controlled trials aren't possible. Whether you are evaluating a policy change via double-difference, identifying impacts at a hard threshold, resolving endogenous variables with instruments, or matching similar profiles to estimate treatment effects, Lattice handles the execution complexity for you. The platform utilizes a specialized three-stage architecture to ensure your analysis remains sound: first, the LLM analyzes your data structure to select the appropriate causal model; second, the deterministic engine runs the heavy math—calculating coefficients, standard errors, and confidence intervals—without variation; third, the LLM presents the result. Crucially, if your data violates fundamental assumptions—such as non-parallel trends, weak instruments, or unbalanced matching—the system triggers an automated 'post-check' and embeds a mandatory warning in your report, preventing you from drawing false conclusions from statistically fragile results.
When to choose this family
- You have data before and after an event and want to determine if the change was actually caused by that intervention.
- You are looking at a hard cutoff or eligibility rule and want to compare individuals just above and below that limit.
- Your data includes an 'instrument'—an external factor that affects who gets treated but not the outcome directly.
- You have observational data and want to compare treated subjects with untreated peers who have identical characteristics.
From Correlation to Causality
These methods shift your focus from simply seeing what happens to understanding why. While basic regression tells you that two variables move together, causal tools incorporate structural checks to guard against lurking variables that might otherwise skew your interpretation of the data.
Unlike standard predictive models, these tools prioritize internal validity. By enforcing constraints like parallel trend testing or common support verification, the methodology forces you to confront whether your data actually supports a causal claim or if it is merely showing a coincidence.
Why This Methodology is Different
The primary difference lies in the 'post-check' layer. Most statistical software provides an answer regardless of whether the mathematical assumptions hold; Lattice, however, explicitly tests for violations—such as McCrary density failures or weak instrument bias—and reports those findings alongside the coefficient.
This approach prevents the common pitfall of relying on a single 'beta' value. By distinguishing between the raw estimate and the validity of the underlying assumption, the platform ensures that the generated plain-language summary always contextualizes the reliability of the result.
Common Interpretation Mistakes
A common mistake is assuming that a statistically significant coefficient is always a valid causal finding. If your data fails a pre-trend test or exhibits weak instruments, that coefficient is effectively noise, regardless of the p-value. Always review the 'concern' flags reported by the LLM before finalizing a decision.
Another error is over-generalizing results. Causal methods often estimate effects for specific subgroups, such as those near a threshold or those who comply with an instrument. Recognizing that your 'LATE' (Local Average Treatment Effect) does not necessarily apply to the entire population is critical for accurate reporting.
Frequently asked questions
- What happens if my analysis fails a 'post-check'?
- If your data violates a core assumption—like failing the parallel trends test in DiD or having weak instruments in 2SLS—the tool marks the model with a concern flag. The LLM then incorporates a mandatory warning in the summary, advising you that the causal claim is unreliable and suggesting specific actions, such as finding a better instrument or performing a sensitivity analysis.
- Do I need to calculate my own bandwidth or match weights?
- No. The deterministic engine handles bandwidth selection for RDD using heuristic approximations or calculates propensity scores and matches for PSM automatically. You can provide your own values if you have specific domain requirements, but the platform defaults to verified settings to ensure the analysis remains reproducible and avoids common configuration errors.