Questions tagged [selection-bias]
Bias introduced by non-random selection of observations, such that the sample is not representative of the underlying population.
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Calculating Inverse Mills Ratio after Probit
I need to compute the Inverse Mills Ratio after the probit command in Stata. From here, I found that predict IMR1, score, will calculate it and store it in IMR1. I ...
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Correcting for selection bias with standardisation/g-computation
Two sets of methods for correcting for selection bias are g-computation (standardisation) and inverse probability of censoring weighting (IPCW). I'm having a difficult time understanding how to apply ...
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Right Way to Sample a Validation Set
I am working on a project that uses training data selection techniques; it involves sampling the training set in some smart way rather than sampling randomly. The goal is to compare different data ...
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Understanding selection bias and endogeneity in marketing
Media mix modelling is concerned with estimating causal impact of marketing investments , a goal which have several challenges. In general, multiple regression models are deployed mapping up total ...
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Is "skewing the data" and "skewing the results" just selection bias?
I recall various conversations with biologists, ecologists, and foresters that I neglected to ask for clarification on at the time. It doesn't occur in any of my statistics references.
Sometimes in ...
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How to improve sample representativeness for longitudinal data collected via an online platform?
I am working with a longitudinal dataset exploring cognitive ageing (e.g., memory performance over time). Participants complete the study annually. Inclusion criteria for this study are 1) UK resident,...
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Can I use Shapley values with metadata (i.e. information about observations that I didn't train my model on)?
I'm training a set of models (random forest/XGBoost) for an ordinal regression task. I'm (tentatively) planning to use Shapley values to infer feature performance.
I also have some metadata that my ...
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How can aggregation be helpful in mitigating bias?
I am working on the estimation assessing the impact of exposure to infrastructure (mainly schooling) on the number of children.
Since I do not have migration data, my colleague recommended that I ...
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Incorporating Inverse Probability Weights in phylogenetic imputation
In Cortes et al. “Sample Selection Bias Correction Theory.” In Algorithmic Learning Theory, 5254:38–53. Lecture Notes in Computer Science. 2008., they describe how inverse probability weights can be ...
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Selection Bias in Conflict Studies
A common critique I have heard levied against conflict studies (research examining the causes, consequences, and solutions to violence such as civil war, terrorism, etc.) is the problem of selection ...
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Selection Bias in Linear Regression Model
Can anyone explain what is the difference between $(\bar{e_1} - \bar{e_0}) $ and ${E(\bar{e_1 }) - E(\bar{e_2 })}$?
I know they are both selection bias, but what are the differences?
Why do we say ...
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Unconfoundedness vs CIA vs selection on observables
I just had a quick question about the CIA, conditional unconfoundedness and secelction on observables only. Do these three terms mean the exact same thing or are there differences between the three?
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Population Estimation And Conditional Probability
Let's say that I have a data set comprised of age data for a large number of individuals, as well as a unique identifier for each individual. For terminology sake let's call this my base population. ...
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How to address selection bias in a diff-in-diff study?
We know that selection bias occurs when the treatment and control groups are not comparable, leading to differences in the outcome that are not solely due to the treatment.
First edit: By selection ...
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Self-selection bias in difference-in-difference estimation
I am hoping to estimate the causal effects of a voluntary employment scheme available for U25 on mental health and financial independence using a difference-in-difference approach (DiD). Because ...
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Heckman correction for correlation estimates
Suppose I observe random $y_{i,1}, y_{i,2}$, and I wish to estimate the correlation between them.
However, the $y_{i,j}$ are observed subject to some sample selection criterion.
That is,
there are ...
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How to model a continuous outcome given two different instances of selection on being observed?
I am looking at a sample of individuals changing their place of residence between 2 time points, and want to look at the effect of different variables (sex, age, etc) on a continuous property of the ...
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Comparing a multi-dose drug to no drug exposure in a cohort study: Censoring events between doses
I am interested in assessing the association between the two doses of a dietary supplement on an event of interest.
The primary exposure is 'two doses of the supplement', and the comparator is 'no ...
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Will Correlation coefficient be impacted by the magnitude of the variables at play?
I am having doubts about whether the correlation between two variables makes sense:
We have two variables we are trying to check for correlation:
monthly level of engagement of our customer base
...
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How to understand random assignment eliminates selection bias in the potential outcomes framework
In Angrist & Pischke's book mostly harmless econometrics, they explain that if the treatment in an RCT $D_i$ is randomly assigned, then $D_i$ is independent of potential outcomes and the following ...
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Is it possible to use Heckman's correction to correct prevalences and means for attrition bias?
I am interested in comparing the characteristics of participants of a longitudinal cohort study at baseline (Sample A) and follow up (Sample B). Some participants were lost to follow up and did not ...
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Selection bias in postmortem data and creating an artificial earlier study endpoint
I want to analyze postmortem (neuropathology) data from dementia patients who are part of a larger ongoing observational study. At the time of the data freeze (i.e. the time at which I access the data)...
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Questions Regarding Sampling Bias
I'm taking a course in R: "Data Analysis in R" on Coursera, and I came across this question during the lecture:
A retail store considering updates to their credit card policies randomly ...
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How to correct for sampling bias in one population when comparing against another
I have two populations that I'd like to compare across certain metrics. However, most members of population A did not respond to our request for data, and those respondents that did are not ...
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Maximum likelihood of Normal density under selection
Consider the density function given by
$$
\left[\dfrac{\gamma_{\leq0} \mathbb{1}(t \leq 0) + \gamma_{>0} \mathbb{1}(t > 0)}{\gamma_{\leq0}\Phi\left(- \mu / \sigma\right) + \gamma_{>0}\Phi\...
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Why does normalizing difference score>0.25 indicates selection bias which cannot be corrected by regression?
I am reading Propensity Score Analysis(2014) by Guo and Fraser chapter 1 section 4. Denote $\Delta_X$ normalizing difference score of covariate $X$.
"Following Imbens and Wooldridge, a $\Delta_X$ ...
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Inverse Mills Ratio Interpretation [closed]
What is the interpretation of inverse mills ratio in Heckman Selection Model ? Why we are including it as an explanatory variable in the OLS estimator?
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Control Group Selection Bias
I found a study that compared minor physical anomalies(MPA) between certain group of patients with the control group to determine if MPAs occur more frequently among these patients compared to the ...
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Estimating interactions from non-interacting features
Suppose I have a sample $\mathcal{D}=\{(\mathbf{x}^{i}, y^{i})\}_{i=1\dots M}$ of binary variables $\mathbf{X}$ ($N$ of them) and a continuous variable $Y$ that I want to predict based on a linear ...
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Nested Cross-Validation with Small dataset
I am currently working with a small dataset (only 175 samples, 45 features) and have been reading on the proper way to cross-validate my model. I had started with a basic cross-validation using a grid ...
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Sampling weights in Cox proportional hazards models
I'd like to use sampling weights in a Cox proportional hazards regression model to address selection due to different response probabilities. I'm calculating the weights as Inverse Probability of ...
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When can we get unbiased estimate given biased data?
There was a recent "hot take" tweet by Andrej Karpathy (without any comment or clarification from the author):
real-world data distribution is ~N(0,1)
good dataset is ~U(-2,2)
It provoked ...
Tim♦
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Selection Models of Publication Bias for Multilevel Meta-analyses?
Are there any suitable selection models of publication bias for multilevel meta-analyses?
I am currently conducting a 3-level meta-analysis and trying to incorporate selection models to assess ...
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Sample Bias in Study
I have following Study statement:
A council wishes to study the digital awareness of its resident senior population (over 65 years), so it questioned in person 50 residents randomly chosen from a ...
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How to show how biased a statistic is in a non-random sample, knowing the parameter in the general population?
I have a convenience sample, and want to show readers how biased it is relative to the population it's taken from. It's absolutely certain that the sample is biased, and I want to give readers as many ...
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Is it true that a larger, representative dataset is always better to use than a smaller, representative dataset?
By "representative" I mean that the data in the dataset faithfully reflects the "underlying signal" a model is trying to tap in to. Is it always true that, as long as increasing ...
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Can I ignore these individuals without introducing bias?
I have a population that falls under 10 classes. Each individual may or may not come with a location - 83% overall have locations and a breakdown by class is:
Class
# individuals
# with location
# ...
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What are the statistical fallacies of illusion of control?
Illusion of control* appears in gambling and events involving randomness. For example, choosing a lottery ticket which has an additional information that participant has a control of choosing, such as ...
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Estimating population means after a selection procedure
The problem is somewhat related to sequential procedures (e.g. Paulson; Gupta and Miescke)
I have a hundred engines generating normal random numbers with true (unknown) means $E_1,E_2,E_3...E_{100}$, ...
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Looking for a type of bias between "Selection bias" and "Framing"
I am looking for a word to describe a certain type of bias, something between Framing and Selection Bias.
I am analyizing a dataset with measurements and their location in Berlin. The dataset includes ...
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effect of vaccination against covid on hospitalization - help building a causal DAG [closed]
During the covid crisis, we have seen explanations of why vaccinated people end up less hospitalized than unvaccinated people under the paradox that there were more vaccinated people hospitalized than ...
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How to accommodate endogeneity after matching?
I am working on a field experiment where assignment to treatment vs. comparison was random, but participation uptake was not. The design is pre-post, and attrition is certainly not MCAR. This is a ...
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What is the difference between selection bias and composition effect?
Groups we seek to compare (e.g., a treatment group and control group) may differ in ways that constrain our ability to do so. Often, potential outcomes may differ systematically across groups, such as ...
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What is it called when an experimenter discards results that are too unexpected?
There is a type of scientific error where an experimenter gets a result significantly different from prior researchers, assumes they made a mistake, and redoes the experiment until they get a more ...
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How to avoid selection bias while updating lead scoring (predictive) model with new data
We developed a standard lead scoring model using logistic regression on couple of months worth data. The model has been working and we have been pushing only top 1/3 leads to sales team basis that. ...
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Treatment Selection based on Treatment Effects
In many observational studies, researchers assume that the treatment decision is based on observable covariates (that is, the ignorability assumption).
In this case, there are diverse methods that can ...
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Why does the best fit model (lower AIC) yield higher p values than models with higher AIC? [closed]
Background: I am running a model selection in R that includes 1, 2, and 3-covariate models. Each model aims to determine the effect of environmental covariates in the occupancy of different species in ...
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How to handle retraining after model introduces bias
Here is my problem.
I am retraining a machine learning model to detect fraudulent purchases.
The training data is based on purchases and the target is whether or not they the purchase was considered ...
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Causal Inference: Selection Bias and Endogeneity [closed]
I've encountered lots of causal inference terms and jargons (under the Neyman-Rubin potential outcome framework), and I had questions regarding their relationships:
I know that exogeneity E(e|X) = 0 ...
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When we up-sample the training set, don't we introduce selection bias?
When doing supervised machine learning in the health or medical domains, we often have a target class that is relatively rare (e.g., prevalence 1-10% of cases). There are a few techniques we can do to ...
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