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On the Use of Two-Way Fixed Effects Regression Models for Causal Inference with Panel Data
Open accessAbstract
Abstract The two-way linear fixed effects regression ( 2FE ) has become a default method for estimating causal effects from panel data. Many applied researchers use the 2FE estimator to adjust for unobserved unit-specific and time-specific confounders at the same time. Unfortunately, we demonstrate that the ability of the 2FE model to simultaneously adjust for these two types of unobserved confounders critically relies upon the assumption of linear additive effects. Another common justification for the use of the 2FE estimator is based on its equivalence to the difference-in-differences estimator under the simplest setting with two groups and two time periods. We show that this equivalence does not hold under more general settings commonly encountered in applied research. Instead, we prove that the multi-period difference-in-differences estimator is equivalent to the weighted 2FE estimator with some observations having negative weights. These analytical results imply that in contrast to the popular belief, the 2FE estimator does not represent a design-based, nonparametric estimation strategy for causal inference. Instead, its validity fundamentally rests on the modeling assumptions.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Instead, we prove that the multi-period difference-in-differences estimator is equivalent to the weighted FE estimator with some observations having negative weights.”
- Also stated in: § . The Multi-period Difference-in-Differences Estimator · boosted
“Instead, the following theorem establishes that the general multi-period DiD estimator given in Equation ( ) is equivalent to a weighted two-way fixed effects regression estimator.”
- Also stated in: § T (Difference-in-Differences · plain
“Second, although the multi-period DiD estimator can be shown to be equivalent to the weighted FE estimator, some control observations will have negative regression weights.”
§ On the Use of Two-Way Fixed Effects Regression Models for Causal Inference with Panel Data > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Also stated in: § . The Multi-period Difference-in-Differences Estimator · boosted
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The next proposition establishes that this two-way matching estimator, which eliminates mismatches within-unit and within-time dimension, can be written as a weighted FE estimator.”
§ On the Use of Two-Way Fixed Effects Regression Models for Causal Inference with Panel Data > Adjustment for Unobserved Confounders
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: The treatment varies within each unit as well as within each time period.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Although it is generally impossible to eliminate all mismatches, in this section we show that we can do so under the difference-in-differences (DiD) design.”
- Also stated in: § . The Multi-period Difference-in-Differences Estimator · plain
“It is worth noting that all three sets only contain control observations, thereby eliminating all mismatches.”
§ On the Use of Two-Way Fixed Effects Regression Models for Causal Inference with Panel Data > The Difference-in-Differences Design
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: The parallel trend assumption holds for the DiD design.
- Also stated in: § . The Multi-period Difference-in-Differences Estimator · plain
In the paper
“The two-way fixed effects estimator defined in Equation ( ) is equivalent to the following matching estimator,”
- Also stated in: § Adjustment for Unobserved Confounders · plain
“The proposition shows that the estimated counterfactual outcome of a given observation, that is,”
§ On the Use of Two-Way Fixed Effects Regression Models for Causal Inference with Panel Data > Adjustment for Unobserved Confounders
What the sources say
- Not matchedcites Imai and Kim
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Also stated in: § Adjustment for Unobserved Confounders · plain
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Therefore, even the weighted FE estimator, which has fewer mismatches than the standard FE estimator, suffers from some mismatches.”
§ On the Use of Two-Way Fixed Effects Regression Models for Causal Inference with Panel Data > P
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The estimator also has an adjustment set whose observations belong to neither the same unit nor the same time period as the observation being matched with.”
§ On the Use of Two-Way Fixed Effects Regression Models for Causal Inference with Panel Data > P
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We show that this equivalence does not hold under more general settings commonly encountered in applied research.”
- Also stated in: § Introduction · plain
“However, we show that this equivalence does not hold under more general settings frequently encountered in applied research.”
§ On the Use of Two-Way Fixed Effects Regression Models for Causal Inference with Panel Data > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Also stated in: § Introduction · plain
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Unfortunately, we demonstrate that the ability of the FE model to simultaneously adjust for these two types of unobserved confounders critically relies upon the assumption of linear additive effects.”
- Also stated in: § Introduction · boosted
“Unfortunately, we show that the FE's ability to simultaneously adjust for the two types of unobserved confounders critically hinges upon the assumption of linear additive effects.”
§ On the Use of Two-Way Fixed Effects Regression Models for Causal Inference with Panel Data > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Also stated in: § Introduction · boosted
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Another common justification is based on the fact that the FE estimator is equivalent to the difference-in-differences estimator under the simplest setting with two groups and two time periods (e.g., Bertrand, Duflo, and Mullainathan ; Angrist and Pischke ).”
- Also stated in: § . The Multi-period Difference-in-Differences Estimator · boosted
“It is well known that the standard nonparametric DiD estimator is numerically equivalent to the FE estimator in the simplest setting, in which there are only two time periods and the treatment is administered only to one group of units in the second time period.”
§ On the Use of Two-Way Fixed Effects Regression Models for Causal Inference with Panel Data > Introduction
What the sources say
- Not matchedcites Bertrand, Duflo, and Mullainathan
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Angrist and Pischke
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Also stated in: § . The Multi-period Difference-in-Differences Estimator · boosted
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Since the regression weights should generally positive, the results of this section shows that the justification of the FE estimator as the DiD estimator is not warranted unless the linearity assumption is imposed.”
§ On the Use of Two-Way Fixed Effects Regression Models for Causal Inference with Panel Data > T (Difference-in-Differences
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
The paper relies on this without support.
The two-way FE regression is a default method for estimating causal effects from panel data among applied researchers.
“The two-way linear fixed effects regression ( FE) has become a default method for estimating causal effects from panel data.”
“Many social scientists use the two-way fixed effects ( FE) regression, or linear regression with unit and time fixed effects, as the default methodology for estimating causal effects from panel data.”
The paper relies on this without support.
Many applied researchers use the FE estimator to adjust for unobserved unit-specific and time-specific confounders at the same time.
“Many applied researchers use the FE estimator to adjust for unobserved unit-specific and time-specific confounders at the same time.”
“Applied researchers o en use the FE regression to adjust for unobserved unit-specific and time-specific confounders at the same time.”
The authors state this as an assumption.
The two types of unobserved confounders (unit-specific and time-specific) are additive and separable, with no interaction between them.
“Thus, although the interaction between these two types of unobserved confounders is assumed to be absent, there is no functional-form restriction on h(•) and f (•).”
“In other words, since the treatment is binary, the model makes no restriction other than the additivity and separability of the two types of unobserved confounders.”
The authors state this as an assumption.
The parallel trend assumption holds for the DiD design.
“To establish the relations between the FE and DiD estimators, we begin by considering the following parallel trend assumption, A (Parallel Trend).”
The authors state this as an assumption.
The treatment varies within each unit as well as within each time period.
“Assume that the treatment varies within each unit as well as within each time period, that is, 0 < T t =1 X i t < T for each i and 0 < N i =1 X i t < N for each t.”
The authors state this as an assumption.
There is at least one treated and control unit, and at least one unit with D_it = 1.
“Assume that there is at least one treated and control unit, that is, 0 < N i =1 T t =1 X i t < NT , and that there is at least one unit with D i t = 1, that is, 0 < N i =1 T t =1 D i t .”
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In the citing paper
“This approach yields an interpretable causal effect under generalizations of the parallel trends assumption and and an additional 'no carryover' assumption that imposes that the potential outcomes depend only on current treatment status and not on the full treatment history.”
From What's trending in difference-in-differences? A synthesis of the recent econometrics literature (2023) · cites this paper as Imai and Kim (2021)
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“de Chaisemartin and D'Haultfœuille (2020) and Imai and Kim (2021) consider settings where units are treated at different times, but do not necessarily require that treatment is an absorbing state.”
From What's trending in difference-in-differences? A synthesis of the recent econometrics literature (2023) · cites this paper as Imai and Kim (2021)
The cited source says what the claim attributes to it.
In the citing paper
“Their estimators intuitively compare changes in outcomes for units whose treatment status changed to other units whose treatment status remained constant over the same periods.”
From What's trending in difference-in-differences? A synthesis of the recent econometrics literature (2023) · cites this paper as Imai and Kim (2021)
Citations
Cites
How Much Should We Trust Differences-in-Differences Estimates?
Bertrand, M., Duflo, E., & Mullainathan, S. (2004) · The Quarterly Journal of Economics · doi:10.1162/003355304772839588 · Cited by 10,641 · cited 2×
“Another common justification is based on the fact that the 2FE estimator is equivalent to the difference-in-differences estimator under the simplest setting with two groups and two time periods (e.g., Bertrand, Duflo, and Mullainathan 2004; Angrist and Pischke 2009).”
MethodMostly Harmless Econometrics: An Empiricist's Companion
Angrist, J. D., & Pischke, J.-S. (2009) · doi:10.1515/9781400829828 · Cited by 9,467 · cited 2×
“Our result is also related to that of Arkhangelsky et al. (2018) who shows that the weighted two-way fixed effects estimator can improve the synthetic control method of Abadie et al.”
MethodResultAbadie, A., Diamond, A., & Hainmueller, J. (2010) · Journal of the American Statistical Association · doi:10.1198/jasa.2009.ap08746 · Cited by 5,513 · cited 1×
“Recently, a number of researchers have extended the synthetical control method of Abadie, Diamond, and Hainmueller (2010) to more general settings (e.g., Xu 2017; Ben-Michael, Feller, and Rothstein 2019).”
BackgroundTwo-Way Fixed Effects Estimators with Heterogeneous Treatment Effects
de Chaisemartin, C., & D’Haultfœuille, X. (2020) · American Economic Review · doi:10.1257/aer.20181169 · Cited by 5,031
Solon, G., Haider, S. J., & Wooldridge, J. M. (2015) · Journal of Human Resources · doi:10.3368/jhr.50.2.301 · Cited by 1,367
Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models
Xu, Y. (2017) · Political Analysis · doi:10.1017/pan.2016.2 · Cited by 938 · cited 1×
“Recently, a number of researchers have extended the synthetical control method of Abadie, Diamond, and Hainmueller (2010) to more general settings (e.g., Xu 2017; Ben-Michael, Feller, and Rothstein 2019).”
BackgroundDesign-based analysis in Difference-In-Differences settings with staggered adoption
Athey, S., & Imbens, G. W. (2022) · Journal of Econometrics · doi:10.1016/j.jeconom.2020.10.012 · Cited by 917 · cited 2×
“In addition, a number of scholars have recently considered causal interpretations of the standard 2FE estimator (see, e.g., Borusyak and Jaravel 2017; Abraham and Sun 2018; Athey and Imbens 2018; Chaisemartin and D’Haultfœuille 2018; Goodman-Bacon 2018).”
BackgroundDifference-in-Differences with Variation in Treatment Timing
Goodman-Bacon, A. (2018) · doi:10.3386/w25018 · Cited by 757
Revisiting Event Study Designs
Borusyak, K., & Jaravel, X. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2826228 · Cited by 463
When Should We Use Unit Fixed Effects Regression Models for Causal Inference with Longitudinal Data?
Imai, K., & Kim, I. (2019) · American Journal of Political Science · doi:10.1111/ajps.12417 · Cited by 407 · cited 5×
“Indeed, by extending the matching framework of Imai and Kim (2019), we show that the simultaneous adjustment for the two types of unobserved confounders cannot be done nonparametrically under the 2FE framework.”
MethodInfluentialSelf-citation
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“To assess whether the robustness of our results depends on the linearity assumption of the two-way fixed effects logistic model (Imai and Kim 2021), we re-estimated our models using the Poisson Pseudo Maximum Likelihood (PPML) approach with high-dimensional fixed effects.”
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“…causal estimates with panel data, recent statistical advances demonstrated that resulting estimates may be biased if model assumptions do not hold (Imai & Kim, 2021); we remain unconvinced that all potential confounders are controlled in our TWFE model due to the complex and multifaceted nature…”
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“…I rely on the following linear two-way fixed-effects model, which – in this two-wave case – is equivalent to a difference-in-differences estimator (Imai and Kim 2021, 412): Here, α i denotes an individual-level unit fixed effect. β t is a year fixed effect that controls for any general time…”
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“TWFE with OLS, WLS, and Poisson regressions also yield substantively similar conclusions, with estimates consistent with “attenuation bias” of this method (Imai & Kim, 2021).”
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“We consider the following two-way linear fixed effects regression model [63].”
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“…effects are 59See, among others: De Chaisemartin and d’Haultfoeuille, 2020; Arkhangelsky et al., 2021; Athey and Imbens, 2021; Sun and Abraham, 2020; Imai and Kim, 2021; Goodman-Bacon, 2021. heterogeneous, such bias arises when already treated units are used as a control group in later periods.”
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“…be problematic since it can assign negative weights, results in biased coefficients, and may even reverse the sign of the overall effect (Borusyak et al. , 2022; de Chaisemartin and D’Haultfœille, 2020; Callaway and Sant’Anna, 2021; Goodman-Bacon, 2021; Imai and Kim, 2021; Sun and Abraham, 2021).”
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“Furthermore, given recent work suggesting limitations of two-way fi xed effects models (e.g., Imai and Kim 2021), I also tested whether the models would be robust to dropping regime-and/or year-fi xed effects.”
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“However, as Imai and Kim (2021) note, causal inference using 2FE regression relies on parametric assumptions that are generally not compatible with TSCS data with heterogeneous treatment periods (as is the case here).”
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“…absence of effect heterogeneity between entry cohorts and appropriateness of using units which have been treated in the past as controls (De Chaisemartin and D’Haultfœuille 2020; Callaway and Sant’Anna 2021; Goodman-Bacon 2021; Imai and Kim 2021; Sun and Abraham 2021; Athey and Imbens 2022).”
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“Most modeling results apply cycle fixed effects only, which allows the inclusion of time-invariant individual GOFO attributes (such as prior military service and type, former JCS, or COCOM) (Imai & Kim, 2021).”
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“Examples include Dube, Dube, and García-Ponce (2013), Truex (2014), Earle and Gehlbach (2015), Hall (2016), and Larreguy and Marshall (2017). Another is to stick with the two-time-period DID and limit the use of additional pre-treatment periods only to the assessment of pre-treatment trends.”
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“15 In contrast to our preferred two-step estimator, the conventional fixed-effects estimator is not robust against effect heterogeneity (De Chaisemartin & D’Haultfœuille, 2023; Goodman-Bacon, 2021; Imai & Kim, 2021; Liu et al., 2024).”
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“The two-way fixed effects regression has become a default method for estimating causal effects from panel data (Imai and Kim 2021).”
MethodLeveraging Causal Reasoning in Educational Data Mining: An Analysis of Brazilian Secondary Education
Filho, R. L. C. S., Brito, K. D. S., & Adeodato, P. J. L. (2023) · Applied Sciences · doi:10.3390/app13085198 · Cited by 6
Disgruntled cadres: How tax reduction undermines rural governance
Hou, L., Liu, M., & Lü, X. (2023) · Governance · doi:10.1111/gove.12799 · Cited by 5
Chizmar, S. J., Parajuli, R., Bruck, S. R., et al. (2023) · Forest Science · doi:10.1093/forsci/fxad042 · Cited by 5
“Recently, there has been discussion among econometricians about the validity of using the traditional DID framework to estimate the impact of heterogeneous treatment effects (Callaway and Sant’Anna 2021; de Chaisemartin and D’Haultfœuille 2020; Imai and Kim 2021).”
Domnich, A., Lapi, F., Orsi, A., Grattagliano, I., Rossi, A., Cricelli, C. A., & Icardi, G. (2024) · BMC Health Services Research · doi:10.1186/s12913-024-11443-9 · Cited by 10
“To account for time-varying (but region-invar-iant) omitted variables, a full set of year dummies was also included [30, 31].”
MethodApeti, A. E., & Gomado, K. M. (2024) · Economics of Transition and Institutional Change · doi:10.1111/ecot.12436 · Cited by 6
Staggered interventions with no control groups
Batomen, B., & Benmarhnia, T. (2024) · International Journal of Epidemiology · doi:10.1093/ije/dyae137 · Cited by 5
Automation and disappearing routine occupations in Japan
Kikuchi, S., Fujiwara, I., & Shirota, T. (2024) · Journal of the Japanese and International Economies · doi:10.1016/j.jjie.2024.101338 · Cited by 5
Revisiting the J-Shape: Human Development and Fertility in the United States
Schubert, H.-A., Dudel, C., Kolobova, M., & Myrskylä, M. (2024) · Demography · doi:10.1215/00703370-11680156 · Cited by 5
School subject choices in adolescence affect political party support
Martin, N. S., Scott, R., & Kappe, R. (2025) · West European Politics · doi:10.1080/01402382.2025.2521582 · Cited by 5
“11 Similarly to the traditional two-way fixed effects estimator used for difference-in-differences analysis with panel data (Imai and Kim 2021), the REWB within-individual estimator addresses the potential bias attributable to time-invariant confounding by including fixed effects for both…”
On the (Mis) Use of the Fixed Effects Estimator
Millimet, D. L., & Bellemare, M. F. (2025) · Oxford Bulletin of Economics and Statistics · doi:10.1111/obes.70031 · Cited by 6
Making Activation for Young Welfare Recipients Mandatory
Dahl, E. S., & Hernæs, Ø. (2022) · Labour · doi:10.1111/labr.12238 · Cited by 6
Subsidizing private childcare in a universal regime
Räsänen, T., & Österbacka, E. (2023) · Review of Economics of the Household · doi:10.1007/s11150-023-09657-7 · Cited by 5
“The binary two-way FE estimator equals ATT under additional assumptions (de Chaisemartin and D ’ Haultf œ uille 2020, Goodman-Bacon 2021, Imai and Kim 2021).”
Marijuana legalization and traffic fatalities revisited
Chen, W., & French, M. T. (2023) · Southern Economic Journal · doi:10.1002/soej.12657 · Cited by 5
House prices and labour productivity growth: Evidence from OECD countries
Gholipour, H. F., Farzanegan, M. R., & Abu-Alfoul, M. N. (2023) · Construction Management and Economics · doi:10.1080/01446193.2023.2291082 · Cited by 5
Do presidents favor co-partisan mayors in the allocation of federal grants?
Ha, H., & Jenkins, J. A. (2024) · Political Science Research and Methods · doi:10.1017/psrm.2024.4 · Cited by 5
Li, Y., Zhang, Y., Ma, R., & Wang, R. (2024) · PLoS ONE · doi:10.1371/journal.pone.0297055 · Cited by 5
Ismail, M. S., & Wilhelmsson, M. H. (2024) · Journal of Housing and the Built Environment · doi:10.1007/s10901-024-10115-8 · Cited by 5
“However, the interpretation of estimated treatment effects can be problematic when we include fixed-area effects as a constant over time, and fixed-time effects are assumed to occur constantly in space (Imai and Kim, 2021).”
Zhu, J., Du, Z., & Ge, Z. (2024) · Governance · doi:10.1111/gove.12881 · Cited by 6
Gender-specific impacts of road accessibility on rural development
Stringer, T., Clavel, E. R. G., & Burelo, M. (2025) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2025.104206 · Cited by 8
Firms and regional favouritism
Vu, T. M., & Yamada, H. (2021) · Economics of Transition and Institutional Change · doi:10.1111/ecot.12308 · Cited by 5
The Power Behind the Pen: Institutional Investors’ Influence on Media during Corporate LitigationPreprint
He, J., Xia, H., Zhao, Y., & Zuo, L. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3584740 · Cited by 5
Damaged Relations: How Treaty Withdrawal Impacts International CooperationPreprint
Schmidt, A. (2021) · doi:10.31235/osf.io/meyau · Cited by 5
A Guide to Dynamic Difference-in-Differences Regressions for Political ScientistsPreprint
Li, Z., & Strezhnev, A. (2024) · doi:10.31235/osf.io/kxw92 · Cited by 5
Liberalizing reforms do not cause suicide: Causal estimation using matching, 1980–2019
Rutar, T., & Rutar, M. (2025) · Journal of Comparative Economics · doi:10.1016/j.jce.2025.01.003 · Cited by 5
Hou, J., Li, X., Chen, F., et al. (2025) · Environment Development and Sustainability · doi:10.1007/s10668-025-06628-z · Cited by 4
Effects of drought on crop yields and agricultural revenue in Southern Brazil
Miyamoto, B. C. B., & Hackmann, C. L. (2025) · Discover Agriculture · doi:10.1007/s44279-025-00425-y · Cited by 4
The lasting earnings losses of COVID-19 short-time work
Vogtenhuber, S., Steiber, N., & Mühlböck, M. (2024) · Research in Social Stratification and Mobility · doi:10.1016/j.rssm.2024.100889 · Cited by 4
Food and peace? Exploring the link between conflict and food insecurity in Africa
d’Agostino, G., Dunne, J. P., & Pieroni, L. (2025) · Defence and Peace Economics · doi:10.1080/10242694.2025.2456798 · Cited by 5
“Using the Imai and Kim (2020); Imai et al. (2021) approach addressed this issue of endogeneity directly. by defining a dummy variable for countries experiencing at least one battle-related event.”
Arora, A., & Singh, K. (2025) · Managerial Finance · doi:10.1108/mf-08-2024-0569 · Cited by 4
The effect of Ukrainian refugees on the local labour markets: the case of Czechia
Postepska, A., & Voloshyna, A. (2025) · Journal of Population Economics · doi:10.1007/s00148-025-01080-9 · Cited by 6
Westlund, H., & Wilhelmsson, M. H. (2025) · Economic Analysis and Policy · doi:10.1016/j.eap.2025.07.025 · Cited by 4
Societal happiness and corporate cash holdings: The contingency role of climate policies
Almustafa, H., Alipur, A., Faizulayev, A., et al. (2025) · Journal of Environmental Management · doi:10.1016/j.jenvman.2025.127722 · Cited by 4
The Legislative Legacy of Voter Identification Laws
Campos, A., Harden, J. J., & Bussing, A. (2024) · The Journal of Politics · doi:10.1086/729970 · Cited by 4
Rural healthcare access and supply constraints: A causal analysis
Melo, V., Sigaud, L., Neilson, E., & Bjoerkheim, M. (2024) · Southern Economic Journal · doi:10.1002/soej.12686 · Cited by 5
Sayantani, S. (2024) · Real Estate Economics · doi:10.1111/1540-6229.12507 · Cited by 5
Zhang, J., van Hugten, J., & Stam, W. (2024) · Small Business Economics · doi:10.1007/s11187-024-00973-5 · Cited by 5
“Time-fixed effects ( 𝜆 t ) capture time-specific factors across all groups (Imai & Kim, 2021).”
Changes in Dental Care Use Following Marital Status Change in the Health and Retirement Study
Hubbard, S., & Cornell, P. Y. (2024) · The Gerontologist · doi:10.1093/geront/gnae151 · Cited by 5
Dlamini, M., & Mbonigaba, J. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1442304 · Cited by 4
“Lastly, when interpreting the results from a two-way FE model, it is essential to understand that the process involves more than just determining the direction of the coefficients; their magnitude is equally important as it quantifies the extent and significance of the impact of each variable (20).”
Quantifying the Water Quality Impacts of Cropland Farming in China: A Satellite Data Approach
Tang, C., Guo, Y., & Feng, L. (2025) · American Journal of Agricultural Economics · doi:10.1111/ajae.12510 · Cited by 5
Access to finance and cost stickiness: Evidence from anti-recharacterization laws
Li, T., Lu, C., & Xu, L. (2025) · Advances in Accounting · doi:10.1016/j.adiac.2025.100816 · Cited by 4
Change-Point Detection and Regularization in Time Series Cross-Sectional Data Analysis
Park, J. H., & Yamauchi, S. (2022) · Political Analysis · doi:10.1017/pan.2022.23 · Cited by 4
Li, B., Chen, J., & Li, S. (2023) · Mathematics · doi:10.3390/math11061531 · Cited by 4
“…turn of the 19th and 20th centuries, Galton and Pearson conceptualized regression, there were a number of regression models for analyzing panel data and exploring the association between dependent variable and regressors (Hsiao [2]; Baltagi [3]; Porter et al. [4]; Zamanzade [5]; Imai and Kim [6]).”
Electoral Systems, Partisan Politics, and Income Redistribution: A Critical Quasi -Experiment
Górecki, M. A., & Pierzgalski, M. (2023) · Comparative Political Studies · doi:10.1177/00104140231169018 · Cited by 3
How does a growing wealth gap affect voting? – Evidence from Sweden
Brännlund, A., & Szulkin, J. (2023) · Electoral Studies · doi:10.1016/j.electstud.2023.102647 · Cited by 4
Kapeller, R., Cohen, J. J., Kollmann, A., et al. (2023) · Energy Economics · doi:10.1016/j.eneco.2023.107028 · Cited by 4
Fan, D., Fuxia, Y. A. N. G., ZHENG, X., & ZHANG, Q. (2023) · 资源科学 · doi:10.18402/resci.2023.08.03 · Cited by 4
Fear or Anger? Leaders’ Childhood War Trauma and Interstate Conflict Initiation
Kim, J. D. (2024) · International Studies Quarterly · doi:10.1093/isq/sqae069 · Cited by 4
Souza, M. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3484635 · Cited by 4
The Effect of Job Loss and Unemployment Insurance on Crime in BrazilPreprint
Pinotti, P., Sampaio, B. R., & Britto, D. G. C. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3606924 · Cited by 4
Unexpected events during survey design and trust in the police: A systematic reviewPreprint
Nägel, C., & Nivette, A. E. (2022) · doi:10.31219/osf.io/ubnaf · Cited by 4
Causal graphical views of fixed effects and random effects models.Preprint
Kim, Y., & Steiner, P. M. (2019) · British Journal of Mathematical & Statistical Psychology · doi:10.31234/osf.io/cxd2n · Cited by 19
Hard-Right Social Media and Civil UnrestPreprint
Karell, D., Linke, A. M., Holland, E. C., & Hendrickson, E. (2021) · doi:10.31235/osf.io/pna5u · Cited by 4
Does Economic Freedom Lighten the Blow? Evidence from the Great Recession in the United StatesPreprint
Callais, J. T., & Pavlik, J. B. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4149724 · Cited by 4
Treaty Withdrawal and the Development of International LawPreprint
Schmidt, A. (2023) · doi:10.31235/osf.io/d432p · Cited by 4
Competition Enforcement and Accounting for Intangible CapitalPreprint
Kepler, J. D., MCCLURE, C. G., & Stewart, C. R. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4861968 · Cited by 4
Seeing is believing: The effects of optometrist scope of practice expansion
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Metadata
- Authors
- Imai, K., & Kim, I. S.
- Affiliations
- Harvard University (US)
- Massachusetts Institute of Technology (US)
- Corresponding author
- Kim, In Song
- Venue
- Political Analysis
- Publisher
- Cambridge University Press (CUP)
- Published
- 2020-11-12
- Volume
- 29
- Issue
- 3
- Pages
- 405-415
- ISSN
- 1047-1987 (ISSN-L)
- 1476-4989
- OpenAlex
- W3098174737
- Semantic Scholar
- 660ac5f400de68ad64a9f31370bc59a30ec98d22
- Cited by
- 623
- References
- 20
- Citations by year
- 2026: 105 · 2025: 142 · 2024: 126 · 2023: 92 · 2022: 68 · 2021: 54 · 2020: 30
- FWCI
- 42.96
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Political Analysis (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- causal inference, panel data, difference-in-differences, unmeasured confounding, negative weights, design-based estimation, model assumptions
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