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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Correcting Sample Selection Bias given actual Distribution
I have two datasets, both from the same population:
The samples from the first survey are quite representative of the underlying truth. However, the second survey comes with a change in distribution ...
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In what instances do we need to worry about Heckman correction for selection bias?
I understand that Heckman selection models attempt to address selection bias by running two-part models. In the oft cited example of determinants of wage offers: 1) we have a group of people who work ...
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How to estimate the age of players correctly?
I have the data of players active on a gaming console and the playtime hours corresponding to the games they have played and their age. I want to analyze the top (say 10) games that the people between ...
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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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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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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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Intended selection bias
Sampling or selection bias is often presented as something that has to be overcome, avoided, or at least appropriately considered because it's a problem otherwise. I wonder how often situations arise (...
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Heckman Selection (Selection vs. Outcome equation)
A common example of the Heckman selection model involves wages, which are only observed if an individual chooses to participate in the labor force. The first stage probit model dependent variable is a ...
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Adjusting for selection bias in a structural topic model
I want to build a structural topic model that adjusts for selection bias into the sample of entities about whom documents are written. I wanted to use the stm R ...
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Regression Discontinuity Design, staggered treatment allocation
I'm unsure if this complex allocation rule is appropriate for RDD. I will have data for a staggered rollout treatment where there will be about 10 rounds of selection over two years for services (...
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Choice and endogeneity
An independent variable is endogenous if it is correlated with the error term (source).
In the regression framework, this may happen (only?) in case of omitted variables, simultaneity, or measurement ...
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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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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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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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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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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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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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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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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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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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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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Logistic regression for case-control studies
If I have designed a study where participants from 3 disease groups of fixed size were being sampled and suppose the three groups A, B and C are of sizes n_A=50, n_B=50 and n_C=100. Group A is a ...
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How to estimate the impact of policy on labor supply if the policy causes death, too?
Suppose people can make only one of three choices A, B, C.
A = Death
B = Survive, Work
C = Survive, Not work
I want to study the impact of an exogenous policy on ...
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Is this referring to the true underlying distribution, or the distribution of our sample?
I am currently studying the paper Learning and Evaluating Classifiers under Sample Selection Bias by Bianca Zadrozny. In the introduction, the author says the following:
One of the most common ...
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Selection bias in categorical independent variable in regression analysis
I have an issue with selection bias in my independent variable and I am uncertain of the best approach to correct for it.
I'm looking at soccer data and trying to make some forecasts on players from ...
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Correcting for known imputation bias
I have a survey sample which includes income values by type of income for a significant number of high-income households. Some of the income data is measured, i.e. present in the responses of the ...
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Can selection bias be solved by including control variables?
Omitted variable bias can be solved by including covariates that are omitted. However, can selection bias also be solved by including covariates?
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IV-estimation vs. Heckman's selection model
I am trying to grasp the difference between IV-estimation and Heckman's selection model.
I do that by considering the following set-up. My outcome if interest is y ...
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How to handle bias due to self-selection in one group
I want to compare two or three social groups across a set of dependent variables, using data from a longitudinal survey.
The problem is that membership in one of the groups leads to a self-selection ...
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conditional-on-positives bias
I am reading the Bad COP section on https://matheusfacure.github.io/python-causality-handbook/07-Beyond-Confounders.html#bad-cop. I am confused if
$$
E[Y|T = 1] - E[Y|T = 0] = \\
E[Y|Y > 0, T = 1]...
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Find correlation from biased observations
I have a set of observations of a variable Z (shown as the colormap) as a function of two other variables A and B. I want to study how Z varies with respect to A, B, and both A and B (eg. if A ...
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Difference in Difference and Selection Into Treatment
Suppose I impose that the true model of some variable of interest is:
$$
Y_{it} = \alpha_i + \beta_t+\tau_{it}D_{it}+\epsilon_{it}
$$
Where $ D_{it} = 1\{E_i \geq t\} $. This is a kind of DID model ...
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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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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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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 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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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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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 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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Regression with endogenous dummy variables
Suppose I have 1,000 observations on variable Y, and I regress Y on just a dummy variable (d), in two different regressions. In the first regression, I set d equal to 0 for the first 500 observations, ...
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Lagged dependent variables: Is it safe to ignore bias over a long time horizon?
Suppose I have a Poisson model which investigates the effect of a county level policy on robbery counts. Here is the basic specification:
$$
\text{log}(y_{it}) = \theta y_{i,t-1} + \sum_i\text{County}...
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Endogenous Sample Selection and Heckit Correction in relation to Research Question
I am researching the funding amounts of start-ups. I am calculating two models:
...
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Truncation based on two variables
I have some data, which has been constructed from multiple API pulls. Observations have an expiry date, $x_i$, which means that observations which expire before the API pull is done do not appear in ...
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How to perform inference on inverse Mills ratio in Heckit estimation?
I want to estimate log(wages). Most wage estimations suffer from sample selection bias.
So I used the two step heckit procedure to correct for it.
The problem is that I get an insignificant inverse ...
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Logistic regression when only TRUE values are observed, but a control sample exists
I'm involved with a creel survey. We have reason to believe that anglers are keeping the larger fish they catch, or at least that a relationship exists between the size of caught fish and the ...
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