A Causal Diagram Tells You What Drives an Outcome. It Doesn't Tell You the Shape of the Effect.
Two teams can agree on exactly which factor drives an outcome and still make opposite decisions, because knowing the cause is not the same as knowing how the effect behaves. A price change, a policy shift, a messaging change: the causal link can be confirmed while the size of the response still depends on direction, magnitude, and conditions. Treating that response as a single flat number is where the highest-stakes decisions go wrong.
The Gap Between "What Causes What" and "How Much, and When"
A causal diagram answers one question: which variables move which other variables. It does not answer whether the size of that move stays constant, or changes depending on direction and severity.
A well-known study of how Federal Reserve policy surprises move equity markets established the causal channel: an unexpected rate move changes discount-rate expectations and earnings expectations, and those changes move stock prices (Journal of Finance, Wiley). What that structure does not settle is whether a rate increase and a rate cut of the same size produce mirror-image effects. In practice they often don't: tightening surprises can move discount-rate expectations by a different amount than easing surprises move them the other way, and the reverse can hold for earnings expectations. A model that assumes symmetry averages across that difference and gets both regimes wrong.
Why "Identify the Cause" Is Only Step One
Research on factor investing makes the same point: confirming that a factor is genuinely causal, rather than merely associated with an outcome, is a necessary step but not a sufficient one (Causal Factor Investing, Cambridge University Press). The economic value sits in correctly describing how the causal relationship behaves: whether it holds steady, switches between regimes, or flattens out at extremes.
That second step is where a linear, one-size-fits-all estimate breaks down: it fails hardest exactly where failure costs most, in large price increases, crisis-level messaging, demand shocks, and other high-magnitude events that live in the tails, not the middle.
Two Ways to Treat the Same Causal Link
| Assume a single, symmetric effect | Test for asymmetry or regime-dependence | |
|---|---|---|
| **What it estimates** | One coefficient applied in both directions and at every magnitude | Separate responses for different directions, magnitudes, or conditions |
| **Where it holds up** | Small, routine changes near the center of past experience | Large or unusual changes, and any decision that hinges on the tails |
| **Where it fails** | Extremes: sharp increases, crisis conditions, rare shocks | Rarely fails outright; costs more analysis and data to run |
| **Cost of being wrong** | Understated downside risk, overstated upside, in the cases with the most money on the line | Wasted effort if the underlying effect turns out to be genuinely uniform |
| **What it requires** | A confirmed causal link | A confirmed causal link, plus enough data to compare conditions or directions |
Most decisions sit close to the center of past experience, where a symmetric estimate is a reasonable approximation; this is not a case for always choosing the more complex option. The judgment call is knowing which kind of decision you're facing before you size it.
How to Tell Which Situation You're In
Three questions separate a decision that can use a single estimate from one that needs a conditional one:
- Does the decision involve a magnitude larger than anything already observed? Extrapolating past the range of past data is where regime-dependence tends to surface.
- Does the direction of the change matter? A price increase and a price decrease of the same size, or a positive and negative message, do not have to produce mirror-image effects; if the decision only makes sense in one direction, test that direction specifically.
- Is the cost of underestimating the effect asymmetric with the cost of overestimating it? If understating downside risk is far more costly than overstating upside, a single averaged estimate is the wrong tool even if statistically adequate.
If the answer to any of these is yes, size the decision using a tested, conditional effect rather than one average number.
Testing the Question Before Betting On It
This is the same discipline behind running a controlled experiment before committing to a decision. Subconscious runs controlled experiments on simulated markets and reports causal effects with quantified uncertainty rather than a single point estimate, making it possible to check whether an effect holds steady across the conditions a decision actually covers (see how Subconscious runs a study). Where warranted, a team can move from that simulated test to validation with real human participants without changing the underlying causal question: two stages of a decision, not a proxy for either one.
The Limit Worth Naming
Confirming that an effect is asymmetric or regime-dependent is a matter of experimental design discipline, not a technique tied to any one tool or workflow. Reviewing published research and prior studies on a topic can suggest where asymmetry is likely, but it describes someone else's conditions, not the one a buyer is about to act on, and does not replace testing the specific decision at hand. A tested, conditional estimate still describes an average response across the cases observed; it is not a guarantee about the one case a team is about to bet on. See how this plays out in past studies.
The habit worth keeping: confirming a cause is the starting point of a decision, not the end of one.