Sensitivity Evaluation for Unobserved Confounding | by Ugur Yildirim | Feb, 2024


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know the unknowable in observational research

Ugur Yildirim

Towards Data Science
  1. Introduction
  2. Drawback Setup
    2.1. Causal Graph
    2.2. Mannequin With and With out Z
    2.3. Energy of Z as a Confounder
  3. Sensitivity Evaluation
    3.1. Objective
    3.2. Robustness Worth
  4. PySensemakr
  5. Conclusion
  6. Acknowledgements
  7. References

The specter of unobserved confounding (aka omitted variable bias) is a infamous downside in observational research. In most observational research, until we will moderately assume that therapy project is as-if random as in a pure experiment, we will by no means be actually sure that we managed for all doable confounders in our mannequin. Because of this, our mannequin estimates may be severely biased if we fail to regulate for an essential confounder–and we wouldn’t even understand it because the unobserved confounder is, nicely, unobserved!

Given this downside, you will need to assess how delicate our estimates are to doable sources of unobserved confounding. In different phrases, it’s a useful train to ask ourselves: how a lot unobserved confounding would there must be for our estimates to drastically change (e.g., therapy impact not statistically important)? Sensitivity evaluation for unobserved confounding is an lively space of analysis, and there are a number of approaches to tackling this downside. On this submit, I’ll cowl a easy linear technique [1] based mostly on the idea of partial that’s extensively relevant to a big spectrum of instances.

2.1. Causal Graph

Allow us to assume that we’ve 4 variables:

  • Y: final result
  • D: therapy
  • X: noticed confounder(s)
  • Z: unobserved confounder(s)

This can be a widespread setting in lots of observational research the place the researcher is concerned about understanding whether or not the therapy of curiosity has an impact on the end result after controlling for doable treatment-outcome confounders.

In our hypothetical setting, the connection between these variables are such that X and Z each have an effect on D and Y, however D has no impact on Y. In different phrases, we’re describing a state of affairs the place the true therapy impact is null. As will turn into clear within the subsequent part, the aim of sensitivity evaluation is with the ability to cause about this therapy impact when we’ve no entry to Z, as we usually gained’t because it’s unobserved. Determine 1 visualizes our setup.

Determine 1: Drawback Setup

2.2. Mannequin With and With out Z

To exhibit the issue that our unobserved Z may cause, I simulated some information consistent with the issue setup described above. You possibly can seek advice from this pocket book for the small print of the simulation.

Since Z can be unobserved in actual life, the one mannequin we will usually match to information is Y~D+X. Allow us to see what outcomes we get if we run that regression.

Primarily based on these outcomes, it looks as if D has a statistically important impact of 0.2686 (p<0.001) per one unit change on Y, which we all know isn’t true based mostly on how we generated the info (no D impact).

Now, let’s see what occurs to our D estimate after we management for Z as nicely. (In actual life, we in fact gained’t be capable to run this extra regression since Z is unobserved however our simulation setting permits us to peek backstage into the true information technology course of.)

As anticipated, controlling for Z appropriately removes the D impact by shrinking the estimate in direction of zero and giving us a p-value that’s not statistically important on the 𝛼=0.05 threshold (p=0.059).

2.3. Energy of Z as a Confounder

At this level, we’ve established that Z is robust sufficient of a confounder to eradicate the spurious D impact because the statistically important D impact disappears after we management for Z. What we haven’t mentioned but is strictly how sturdy Z is as a confounder. For this, we’ll make the most of a helpful statistical idea known as partial , which quantifies the proportion of variation {that a} given variable of curiosity can clarify that may’t already be defined by the present variables in a mannequin. In different phrases, partial tells us the added explanatory energy of that variable of curiosity, above and past the opposite variables which can be already within the mannequin. Formally, it may be outlined as follows

the place RSS_reduced is the residual sum of squares from the mannequin that doesn’t embrace the variable(s) of curiosity and RSS_full is the residual sum of squares from the mannequin that features the variable(s) of curiosity.

In our case, the variable of curiosity is Z, and we want to know what quantity of the variation in Y and D that Z can clarify that may’t already be defined by the present variables. Extra exactly, we have an interest within the following two partial values

the place (1) quantifies the proportion of variance in Y that may be defined by Z that may’t already be defined by D and X (so the decreased mannequin is Y~D+X and the complete mannequin is Y~D+X+Z), and (2) quantifies the proportion of variance in D that may be defined by Z that may’t already be defined by X (so the decreased mannequin is D~X and the complete mannequin is D~X+Z).

Now, allow us to see how strongly related Z is with D and Y in our information by way of partial .

It seems that Z explains 16% of the variation in Y that may’t already be defined by D and X (that is partial equation #1 above), and 20% of the variation in D that may’t already be defined by X (that is partial equation #2 above).

3.1. Objective

As we mentioned within the earlier part, unobserved confounding poses an issue in actual analysis settings exactly as a result of, in contrast to in our simulation setting, Z can’t be noticed. In different phrases, we’re caught with the mannequin Y~D+X, having no solution to know what our outcomes would have been if we might run the mannequin Y~D+X+Z as a substitute. So, what can we do?

Intuitively, an inexpensive sensitivity evaluation method ought to be capable to inform us that if a Z such because the one we’ve in our information had been to exist, it will nullify our outcomes. Do not forget that our Z explains 16% of the variation in Y and 20% of the variation in D that may’t be defined by noticed variables. Due to this fact, we count on sensitivity evaluation to inform us {that a} hypothetical Z-like confounder of comparable energy can be sufficient to eradicate the statistically important D impact.

However how can we calculate that the unobserved confounder’s energy needs to be on this 16–20% vary within the partial scale with out ever accessing it? Enter robustness worth.

3.2. Robustness Worth

Robustness worth (RV) formalizes the thought we talked about above of figuring out the mandatory energy of a hypothetical unobserved confounder that would nullify our outcomes. The usefulness of RV emanates from the truth that we solely want our observable mannequin Y~D+X and never the unobservable mannequin Y~D+X+Z to have the ability to calculate it.

Formally, we will write down as follows the RV that quantifies how sturdy unobserved confounding must be to alter our noticed statistical significance of the therapy impact (if the notation is an excessive amount of to comply with, simply keep in mind the important thing concept that the RV is a measure of the energy of confounding wanted to alter our outcomes)

Picture by writer, equations based mostly on [1], see pages 49–52

the place

  • 𝛼 is our chosen significance stage (typically set to 0.05 or 5%),
  • q determines the % discount q*100% in significance that we care about (typically set to 1, since we often care about confounding that would cut back statistical significance by 1*100%=100% therefore rendering it not statistically important),
  • t_betahat_treat is the noticed t-value of our therapy from the mannequin Y~D+X (which is 8.389 on this case as may be seen from the regression outcomes above),
  • df is our levels of freedom (which is 1000–3=997 on this case since we simulated 1000 samples and are estimating 3 parameters together with the intercept), and
  • t*_alpha,df-1 is the t-value threshold related to a given 𝛼 and df-1 (1.96 if 𝛼 is ready to 0.05).

We are actually able to calculate the RV in our personal information utilizing solely the noticed mannequin Y~D+X (res_ydx).

It’s by no struck of luck that our RV (18%) falls proper within the vary of the partial values we calculated for Y~Z|D,X (16%) and D~Z|X (20%) above. What the RV is telling us right here is that, even with none express data of Z, we will nonetheless cause that any unobserved confounder wants, on common, no less than 18% energy within the partial scale vis-à-vis each the therapy and the end result to have the ability to nullify our statistically important outcome.

The explanation why the RV isn’t 16% or 20% however falls someplace in between (18%) is that it’s designed to be a single quantity that summarizes the mandatory energy of the confounder with each the end result and the therapy, so 18% makes excellent sense given what we all know concerning the information. You possibly can give it some thought like this: because the technique doesn’t have entry to the precise numbers 16% and 20% when calculating the RV, it’s doing its greatest to quantify the energy of the confounder by assigning 18% to each partial values (Y~Z|D,X and D~Z|X), which isn’t too far off from the reality in any respect and really does an excellent job summarizing the energy of the confounder.

In fact, in actual life we gained’t have the Z variable to double verify that our RV is appropriate, however seeing how the 2 outcomes align right here ought to no less than provide you with some confidence within the technique. Lastly, as soon as we calculate the RV, we must always take into consideration whether or not an unobserved confounder of that energy is believable. In our case, the reply is ‘sure’ as a result of we’ve entry to the info technology course of, however to your particular real-life utility, the existence of such a powerful confounder is likely to be an unreasonable assumption. This might be excellent news for you since no life like unobserved confounder might drastically change your outcomes.

The sensitivity evaluation approach described above has already been carried out with all of its bells and whistles as a Python bundle below the identify PySensemakr (R, Stata, and Shiny App variations exist as nicely). For instance, to get the very same outcome that we manually calculated within the earlier part, we will merely run the next code chunk.

Notice that “Robustness Worth, q = 1 alpha = 0.05” is 0.184, which is strictly what we calculated above. Along with the RV for statistical significance, the bundle additionally offers the RV that’s wanted for the coefficient estimate itself to shrink to 0. Not surprisingly, unobserved confounding must be even bigger for this to occur (0.233 vs 0.184).

The bundle additionally offers contour plots for the 2 partial values, which permits for an intuitive visible show of sensitivity to doable ranges of confounding with the therapy and the end result (on this case, it shouldn’t be stunning to see that the x/y-axis worth pairs that meet the purple dotted line embrace 0.18/0.18 in addition to 0.20/0.16).

One may even add benchmark values to the contour plot as proxies for doable quantities of confounding. In our case, since we solely have one noticed covariate X, we will set our benchmarks to be 0.25x, 0.5x and 1x as sturdy as that noticed covariate. The ensuing plot tells us {that a} confounder that’s half as sturdy as X needs to be sufficient to nullify our statistically important outcome (because the “0.5x X” worth falls proper on the purple dotted line).

Lastly, I want to notice that whereas the simulated information on this instance used a steady therapy variable, in observe the strategy works for any sort of therapy variable together with binary remedies. However, the end result variable technically must be a steady one since we’re working within the OLS framework. Nonetheless, the strategy can nonetheless be used even with a binary final result if we mannequin it utilizing OLS (that is known as a LPM [2]).

The likelihood that our impact estimate could also be biased resulting from unobserved confounding is a typical hazard in observational research. Regardless of this potential hazard, observational research are a significant instrument in information science as a result of randomization merely isn’t possible in lots of instances. Due to this fact, you will need to know the way we will tackle the problem of unobserved confounding by operating sensitivity analyses to see how strong our estimates are to potential such confounding.

The robustness worth technique by Cinelli and Hazlett mentioned on this submit is a straightforward and intuitive method to sensitivity evaluation formulated in a well-recognized linear mannequin framework. In case you are concerned about studying extra concerning the technique, I extremely suggest having a look on the unique paper and the bundle documentation the place you possibly can study many extra fascinating functions of the strategy reminiscent of ‘excessive state of affairs’ evaluation.

There are additionally many different approaches to sensitivity evaluation for unobserved confounding, and I would really like briefly point out a few of them right here for readers who want to proceed studying extra on this matter. One versatile approach is the E-value developed by VanderWeele and Ding that formulates the issue by way of threat ratios [3] (carried out in R right here). One other approach is the Austen plot developed by Veitch and Zaveri based mostly on the ideas of partial and propensity rating [4] (carried out in Python right here), and one more current method is by Chernozhukov et al [5] (carried out in Python right here).

I want to thank Chad Hazlett for answering my query associated to utilizing the strategy with binary outcomes and Xinyi Zhang for offering numerous worthwhile suggestions on the submit. Until in any other case famous, all photographs are by the writer.

[1] C. Cinelli and C. Hazlett, Making Sense of Sensitivity: Extending Omitted Variable Bias (2019), Journal of the Royal Statistical Society

[2] J. Murray, Linear Chance Mannequin, Murray’s private web site

[3] T. VanderWeele and P. Ding, Sensitivity Evaluation in Observational Analysis: Introducing the E-Worth (2017), Annals of Inner Drugs

[4] V. Veitch and A. Zaveri, Sense and Sensitivity Evaluation: Easy Publish-Hoc Evaluation of Bias Resulting from Unobserved Confounding (2020), NeurIPS

[5] V. Chernozhukov, C. Cinelli, W. Newey, A. Sharma, and V. Syrgkanis, Lengthy Story Brief: Omitted Variable Bias in Causal Machine Studying (2022), NBER



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